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25 pages, 6051 KiB

Open AccessArticle

Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data

by Georgios Pipelidis, Omid Reza Moslehi Rad, Dorota Iwaszczuk, Christian Prehofer and Urs Hugentobler

Sensors 2018, 18(2), 480; https://doi.org/10.3390/s18020480 - 6 Feb 2018

Cited by 6 | Viewed by 4815

Abstract

In this paper, we present our novel approach for the crowdsourced dynamic vertical mapping of buildings. For achieving this, we use the barometric sensor of smartphones to estimate altitude differences and the moment of the outdoor to indoor transition to extract reference pressure. [...] Read more.

In this paper, we present our novel approach for the crowdsourced dynamic vertical mapping of buildings. For achieving this, we use the barometric sensor of smartphones to estimate altitude differences and the moment of the outdoor to indoor transition to extract reference pressure. We have identified the outdoor–indoor transition (OITransition) via the fusion of four different sensors. Our approach has been evaluated extensively over a period of 6 months in different humidity, temperature, and cloud-coverage situations, as well as over different hours of the day, and it is found that it can always predict the correct number of floors, while it can approximate the altitude with an average error of 0.5 m. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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21 pages, 4181 KiB

Open AccessArticle

Collaborative Indoor Access Point Localization Using Autonomous Mobile Robot Swarm

by Fahed Awad, Muhammad Naserllah, Ammar Omar, Alaa Abu-Hantash and Abrar Al-Taj

Sensors 2018, 18(2), 407; https://doi.org/10.3390/s18020407 - 31 Jan 2018

Cited by 16 | Viewed by 6795

Abstract

Localization of access points has become an important research problem due to the wide range of applications it addresses such as dismantling critical security threats caused by rogue access points or optimizing wireless coverage of access points within a service area. Existing proposed [...] Read more.

Localization of access points has become an important research problem due to the wide range of applications it addresses such as dismantling critical security threats caused by rogue access points or optimizing wireless coverage of access points within a service area. Existing proposed solutions have mostly relied on theoretical hypotheses or computer simulation to demonstrate the efficiency of their methods. The techniques that rely on estimating the distance using samples of the received signal strength usually assume prior knowledge of the signal propagation characteristics of the indoor environment in hand and tend to take a relatively large number of uniformly distributed random samples. This paper presents an efficient and practical collaborative approach to detect the location of an access point in an indoor environment without any prior knowledge of the environment. The proposed approach comprises a swarm of wirelessly connected mobile robots that collaboratively and autonomously collect a relatively small number of non-uniformly distributed random samples of the access point’s received signal strength. These samples are used to efficiently and accurately estimate the location of the access point. The experimental testing verified that the proposed approach can identify the location of the access point in an accurate and efficient manner. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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19 pages, 4440 KiB

Open AccessArticle

EKF–GPR-Based Fingerprint Renovation for Subset-Based Indoor Localization with Adjusted Cosine Similarity

by Junhua Yang, Yong Li, Wei Cheng, Yang Liu and Chenxi Liu

Sensors 2018, 18(1), 318; https://doi.org/10.3390/s18010318 - 22 Jan 2018

Cited by 18 | Viewed by 6843

Abstract

Received Signal Strength Indicator (RSSI) localization using fingerprint has become a prevailing approach for indoor localization. However, the fingerprint-collecting work is repetitive and time-consuming. After the original fingerprint radio map is built, it is laborious to upgrade the radio map. In this paper, [...] Read more.

Received Signal Strength Indicator (RSSI) localization using fingerprint has become a prevailing approach for indoor localization. However, the fingerprint-collecting work is repetitive and time-consuming. After the original fingerprint radio map is built, it is laborious to upgrade the radio map. In this paper, we describe a Fingerprint Renovation System (FRS) based on crowdsourcing, which avoids the use of manual labour to obtain the up-to-date fingerprint status. Extended Kalman Filter (EKF) and Gaussian Process Regression (GPR) in FRS are combined to calculate the current state based on the original fingerprinting radio map. In this system, a method of subset acquisition also makes an immediate impression to reduce the huge computation caused by too many reference points (RPs). Meanwhile, adjusted cosine similarity (ACS) is employed in the online phase to solve the issue of outliers produced by cosine similarity. Both experiments and analytical simulation in a real Wireless Fidelity (Wi-Fi) environment indicate the usefulness of our system to significant performance improvements. The results show that FRS improves the accuracy by 19.6% in the surveyed area compared to the radio map un-renovated. Moreover, the proposed subset algorithm can bring less computation. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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21 pages, 8051 KiB

Open AccessArticle

An IMU-Aided Body-Shadowing Error Compensation Method for Indoor Bluetooth Positioning

by Zhongliang Deng, Xiao Fu and Hanhua Wang

Sensors 2018, 18(1), 304; https://doi.org/10.3390/s18010304 - 20 Jan 2018

Cited by 18 | Viewed by 5839

Abstract

Research on indoor positioning technologies has recently become a hotspot because of the huge social and economic potential of indoor location-based services (ILBS). Wireless positioning signals have a considerable attenuation in received signal strength (RSS) when transmitting through human bodies, which would cause [...] Read more.

Research on indoor positioning technologies has recently become a hotspot because of the huge social and economic potential of indoor location-based services (ILBS). Wireless positioning signals have a considerable attenuation in received signal strength (RSS) when transmitting through human bodies, which would cause significant ranging and positioning errors in RSS-based systems. This paper mainly focuses on the body-shadowing impairment of RSS-based ranging and positioning, and derives a mathematical expression of the relation between the body-shadowing effect and the positioning error. In addition, an inertial measurement unit-aided (IMU-aided) body-shadowing detection strategy is designed, and an error compensation model is established to mitigate the effect of body-shadowing. A Bluetooth positioning algorithm with body-shadowing error compensation (BP-BEC) is then proposed to improve both the positioning accuracy and the robustness in indoor body-shadowing environments. Experiments are conducted in two indoor test beds, and the performance of both the BP-BEC algorithm and the algorithms without body-shadowing error compensation (named no-BEC) is evaluated. The results show that the BP-BEC outperforms the no-BEC by about 60.1% and 73.6% in terms of positioning accuracy and robustness, respectively. Moreover, the execution time of the BP-BEC algorithm is also evaluated, and results show that the convergence speed of the proposed algorithm has an insignificant effect on real-time localization. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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20 pages, 1699 KiB

Open AccessArticle

Walking Distance Estimation Using Walking Canes with Inertial Sensors

by Duc Cong Dang and Young Soo Suh

Sensors 2018, 18(1), 230; https://doi.org/10.3390/s18010230 - 15 Jan 2018

Cited by 13 | Viewed by 6095

Abstract

A walking distance estimation algorithm for cane users is proposed using an inertial sensor unit attached to various positions on the cane. A standard inertial navigation algorithm using an indirect Kalman filter was applied to update the velocity and position of the cane [...] Read more.

A walking distance estimation algorithm for cane users is proposed using an inertial sensor unit attached to various positions on the cane. A standard inertial navigation algorithm using an indirect Kalman filter was applied to update the velocity and position of the cane during movement. For quadripod canes, a standard zero-velocity measurement-updating method is proposed. For standard canes, a velocity-updating method based on an inverted pendulum model is proposed. The proposed algorithms were verified by three walking experiments with two different types of canes and different positions of the sensor module. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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11 pages, 4070 KiB

Open AccessArticle

Closed-Form Algorithm for 3-D Near-Field OFDM Signal Localization under Uniform Circular Array

by Xiaolong Su, Zhen Liu, Xin Chen and Xizhang Wei

Sensors 2018, 18(1), 226; https://doi.org/10.3390/s18010226 - 14 Jan 2018

Cited by 7 | Viewed by 5511

Abstract

Due to its widespread application in communications, radar, etc., the orthogonal frequency division multiplexing (OFDM) signal has become increasingly urgent in the field of localization. Under uniform circular array (UCA) and near-field conditions, this paper presents a closed-form algorithm based on phase difference [...] Read more.

Due to its widespread application in communications, radar, etc., the orthogonal frequency division multiplexing (OFDM) signal has become increasingly urgent in the field of localization. Under uniform circular array (UCA) and near-field conditions, this paper presents a closed-form algorithm based on phase difference for estimating the three-dimensional (3-D) location (azimuth angle, elevation angle, and range) of the OFDM signal. In the algorithm, considering that it is difficult to distinguish the frequency of the OFDM signal’s subcarriers and the phase-based method is always affected by errors of the frequency estimation, this paper employs sparse representation (SR) to obtain the super-resolution frequencies and the corresponding phases of subcarriers. Further, as the phase differences of the adjacent sensors including azimuth angle, elevation angle and range parameters can be expressed as indefinite equations, the near-field OFDM signal’s 3-D location is obtained by employing the least square method, where the phase differences are based on the average of the estimated subcarriers. Finally, the performance of the proposed algorithm is demonstrated by several simulations. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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14 pages, 3511 KiB

Open AccessArticle

Plils: A Practical Indoor Localization System through Less Expensive Wireless Chips via Subregion Clustering

by Xiaolong Li, Yifu Yang, Jun Cai, Yun Deng, Junfeng Yang, Xinmin Zhou and Lina Tan

Sensors 2018, 18(1), 205; https://doi.org/10.3390/s18010205 - 12 Jan 2018

Cited by 4 | Viewed by 4997

Abstract

Reducing costs is a pragmatic method for promoting the widespread usage of indoor localization technology. Conventional indoor localization systems (ILSs) exploit relatively expensive wireless chips to measure received signal strength for positioning. Our work is based on a cheap and widely-used commercial off-the-shelf [...] Read more.

Reducing costs is a pragmatic method for promoting the widespread usage of indoor localization technology. Conventional indoor localization systems (ILSs) exploit relatively expensive wireless chips to measure received signal strength for positioning. Our work is based on a cheap and widely-used commercial off-the-shelf (COTS) wireless chip, i.e., the Nordic Semiconductor nRF24LE1, which has only several output power levels, and proposes a new power level based-ILS, called Plils. The localization procedure incorporates two phases: an offline training phase and an online localization phase. In the offline training phase, a self-organizing map (SOM) is utilized for dividing a target area into k subregions, wherein their grids in the same subregion have similar fingerprints. In the online localization phase, the support vector machine (SVM) and back propagation (BP) neural network methods are adopted to identify which subregion a tagged object is located in, and calculate its exact location, respectively. The reasonable value for k has been discussed as well. Our experiments show that Plils achieves 75 cm accuracy on average, and is robust to indoor obstacles. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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20 pages, 6717 KiB

Open AccessArticle

Experimental Evaluation of UWB Indoor Positioning for Sport Postures

by Matteo Ridolfi, Stef Vandermeeren, Jense Defraye, Heidi Steendam, Joeri Gerlo, Dirk De Clercq, Jeroen Hoebeke and Eli De Poorter

Sensors 2018, 18(1), 168; https://doi.org/10.3390/s18010168 - 9 Jan 2018

Cited by 98 | Viewed by 9259

Abstract

Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. [...] Read more.

Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. Of the above-mentioned forms of radio frequency (RF) technology, UWB is considered one of the most accurate approaches because it can provide positioning estimates with centimeter-level accuracy. However, it is not yet known whether UWB can also offer such accurate position estimates during strenuous dynamic activities in which moves are characterized by fast changes in direction and velocity. To answer this question, this paper investigates the capabilities of UWB indoor localization systems for tracking athletes during their complex (and most of the time unpredictable) movements. To this end, we analyze the impact of on-body tag placement locations and human movement patterns on localization accuracy and communication reliability. Moreover, two localization algorithms (particle filter and Kalman filter) with different optimizations (bias removal, non-line-of-sight (NLoS) detection, and path determination) are implemented. It is shown that although the optimal choice of optimization depends on the type of movement patterns, some of the improvements can reduce the localization error by up to 31%. Overall, depending on the selected optimization and on-body tag placement, our algorithms show good results in terms of positioning accuracy, with average errors in position estimates of 20 cm. This makes UWB a suitable approach for tracking dynamic athletic activities. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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19 pages, 4872 KiB

Open AccessArticle

Accurate 3D Positioning for a Mobile Platform in Non-Line-of-Sight Scenarios Based on IMU/Magnetometer Sensor Fusion

by Hendrik Hellmers, Zakaria Kasmi, Abdelmoumen Norrdine and Andreas Eichhorn

Sensors 2018, 18(1), 126; https://doi.org/10.3390/s18010126 - 4 Jan 2018

Cited by 21 | Viewed by 7312

Abstract

In recent years, a variety of real-time applications benefit from services provided by localization systems due to the advent of sensing and communication technologies. Since the Global Navigation Satellite System (GNSS) enables localization only outside buildings, applications for indoor positioning and navigation use [...] Read more.

In recent years, a variety of real-time applications benefit from services provided by localization systems due to the advent of sensing and communication technologies. Since the Global Navigation Satellite System (GNSS) enables localization only outside buildings, applications for indoor positioning and navigation use alternative technologies. Ultra Wide Band Signals (UWB), Wireless Local Area Network (WLAN), ultrasonic or infrared are common examples. However, these technologies suffer from fading and multipath effects caused by objects and materials in the building. In contrast, magnetic fields are able to pass through obstacles without significant propagation errors, i.e. in Non-Line of Sight Scenarios (NLoS). The aim of this work is to propose a novel indoor positioning system based on artificially generated magnetic fields in combination with Inertial Measurement Units (IMUs). In order to reach a better coverage, multiple coils are used as reference points. A basic algorithm for three-dimensional applications is demonstrated as well as evaluated in this article. The established system is then realized by a sensor fusion principle as well as a kinematic motion model on the basis of a Kalman filter. Furthermore, a pressure sensor is used in combination with an adaptive filtering method to reliably estimate the platform’s altitude. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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2366 KiB

Open AccessArticle

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

by José A. Paredes, Fernando J. Álvarez, Teodoro Aguilera and José M. Villadangos

Sensors 2018, 18(1), 89; https://doi.org/10.3390/s18010089 - 30 Dec 2017

Cited by 77 | Viewed by 10589

Abstract

This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential [...] Read more.

This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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5217 KiB

Open AccessArticle

A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks

by Wei Zheng, Xiaoyong Yan, Wei Zhao and Chengshan Qian

Sensors 2017, 17(12), 2959; https://doi.org/10.3390/s17122959 - 20 Dec 2017

Cited by 9 | Viewed by 4645

Abstract

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which [...] Read more.

A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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8245 KiB

Open AccessArticle

A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

by Vicente Cantón Paterna, Anna Calveras Augé, Josep Paradells Aspas and María Alejandra Pérez Bullones

Sensors 2017, 17(12), 2927; https://doi.org/10.3390/s17122927 - 16 Dec 2017

Cited by 177 | Viewed by 14608

Abstract

Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength [...] Read more.

Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength Indicator (RSSI) fluctuations due to the behaviour of the channels and the multipath effect, that lead to poor precision. In order to mitigate these effects, in this paper we propose and implement a real Indoor Positioning System based on Bluetooth Low Energy, that improves accuracy while reducing power consumption and costs. The three main proposals are: frequency diversity, Kalman filtering and a trilateration method what we have denominated “weighted trilateration”. The analysis of the results proves that all the proposals improve the precision of the system, which goes up to 1.82 m 90% of the time for a device moving in a middle-size room and 0.7 m for static devices. Furthermore, we have proved that the system is scalable and efficient in terms of cost and power consumption. The implemented approach allows using a very simple device (like a SensorTag) on the items to locate. The system enables a very low density of anchor points or references and with a precision better than existing solutions. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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4504 KiB

Open AccessArticle

Early Steps in Automated Behavior Mapping via Indoor Sensors

by Taner Arsan and Orcun Kepez

Sensors 2017, 17(12), 2925; https://doi.org/10.3390/s17122925 - 16 Dec 2017

Cited by 10 | Viewed by 8225

Abstract

Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies, we chose Wi-Fi, BLE beacon and ultra-wide band (UWB) [...] Read more.

Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies, we chose Wi-Fi, BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m² test area (6 m × 6 m), 0.86 m for the BLE beacon in a 37.44 m² test area (9.6 m × 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m² test area (6 m × 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m² test area (7.35 m × 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally, we demonstrated the use of ultra-wide band sensor technology for BM. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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9330 KiB

Open AccessArticle

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach

by Mengyun Liu, Ruizhi Chen, Deren Li, Yujin Chen, Guangyi Guo, Zhipeng Cao and Yuanjin Pan

Sensors 2017, 17(12), 2847; https://doi.org/10.3390/s17122847 - 8 Dec 2017

Cited by 64 | Viewed by 8468

Abstract

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, [...] Read more.

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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14406 KiB

Open AccessArticle

A Robust Indoor/Outdoor Navigation Filter Fusing Data from Vision and Magneto-Inertial Measurement Unit

by David Caruso, Alexandre Eudes, Martial Sanfourche, David Vissière and Guy Le Besnerais

Sensors 2017, 17(12), 2795; https://doi.org/10.3390/s17122795 - 4 Dec 2017

Cited by 19 | Viewed by 5230

Abstract

Visual-inertial Navigation Systems (VINS) are nowadays used for robotic or augmented reality applications. They aim to compute the motion of the robot or the pedestrian in an environment that is unknown and does not have specific localization infrastructure. Because of the low quality [...] Read more.

Visual-inertial Navigation Systems (VINS) are nowadays used for robotic or augmented reality applications. They aim to compute the motion of the robot or the pedestrian in an environment that is unknown and does not have specific localization infrastructure. Because of the low quality of inertial sensors that can be used reasonably for these two applications, state of the art VINS rely heavily on the visual information to correct at high frequency the drift of inertial sensors integration. These methods struggle when environment does not provide usable visual features, such than in low-light of texture-less areas. In the last few years, some work have been focused on using an array of magnetometers to exploit opportunistic stationary magnetic disturbances available indoor in order to deduce a velocity. This led to Magneto-inertial Dead-reckoning (MI-DR) systems that show interesting performance in their nominal conditions, even if they can be defeated when the local magnetic gradient is too low, for example outdoor. We propose in this work to fuse the information from a monocular camera with the MI-DR technique to increase the robustness of both traditional VINS and MI-DR itself. We use an inverse square root filter inspired by the MSCKF algorithm and describe its structure thoroughly in this paper. We show navigation results on a real dataset captured by a sensor fusing a commercial-grade camera with our custom MIMU (Magneto-inertial Measurment Unit) sensor. The fused estimate demonstrates higher robustness compared to pure VINS estimate, specially in areas where vision is non informative. These results could ultimately increase the working domain of mobile augmented reality systems. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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7838 KiB

Open AccessArticle

A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers

by Jun Mo, Zhongliang Deng, Buyun Jia and Xinmei Bian

Sensors 2017, 17(12), 2783; https://doi.org/10.3390/s17122783 - 1 Dec 2017

Cited by 7 | Viewed by 4380

Abstract

Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing [...] Read more.

Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing CMMB signal does not have the meter positioning capability. So, the CMMB system has been modified to achieve meter positioning capability by multiplexing the CMMB signal and pseudo codes in the same frequency band. The time difference of arrival (TDOA) estimation method is used in base station positioning receivers. Due to the influence of a complex fading channel and the limited bandwidth of receivers, the regular tracking method based on pseudo code ranging is difficult to provide continuous and accurate TDOA estimations. A pseudorange measurement scheme based on snapshot is proposed to solve the problem. This algorithm extracts the TDOA estimation from the stored signal fragments, and utilizes the Taylor expansion of the autocorrelation function to improve the TDOA estimation accuracy. Monte Carlo simulations and real data tests show that the proposed algorithm can significantly reduce the TDOA estimation error for base station positioning receivers, and then the modified CMMB system achieves meter positioning accuracy. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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7765 KiB

Open AccessArticle

Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting

by Joaquín Torres-Sospedra and Adriano Moreira

Sensors 2017, 17(12), 2736; https://doi.org/10.3390/s17122736 - 27 Nov 2017

Cited by 42 | Viewed by 7246

Abstract

Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal [...] Read more.

Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A 2.5D Map-Based Mobile Robot Localization via Cooperation of Aerial and Ground Robots

by Tae Hyeon Nam, Jae Hong Shim and Young Im Cho

Sensors 2017, 17(12), 2730; https://doi.org/10.3390/s17122730 - 25 Nov 2017

Cited by 24 | Viewed by 10011

Abstract

Recently, there has been increasing interest in studying the task coordination of aerial and ground robots. When a robot begins navigation in an unknown area, it has no information about the surrounding environment. Accordingly, for robots to perform tasks based on location information, [...] Read more.

Recently, there has been increasing interest in studying the task coordination of aerial and ground robots. When a robot begins navigation in an unknown area, it has no information about the surrounding environment. Accordingly, for robots to perform tasks based on location information, they need a simultaneous localization and mapping (SLAM) process that uses sensor information to draw a map of the environment, while simultaneously estimating the current location of the robot on the map. This paper aims to present a localization method based in cooperation between aerial and ground robots in an indoor environment. The proposed method allows a ground robot to reach accurate destination by using a 2.5D elevation map built by a low-cost RGB-D (Red Green and Blue-Depth) sensor and 2D Laser sensor attached onto an aerial robot. A 2.5D elevation map is formed by projecting height information of an obstacle using depth information obtained by the RGB-D sensor onto a grid map, which is generated by using the 2D Laser sensor and scan matching. Experimental results demonstrate the effectiveness of the proposed method for its accuracy in location recognition and computing speed. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting

by Haiyong Luo, Fang Zhao, Mengling Jiang, Hao Ma and Yuexia Zhang

Sensors 2017, 17(11), 2678; https://doi.org/10.3390/s17112678 - 20 Nov 2017

Cited by 49 | Viewed by 6531

Abstract

A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems; however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by [...] Read more.

A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems; however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by external experts excludes quick deployment and prevents widespread utilization of indoor localization systems. Here, we propose an algorithm for the automatic construction of an indoor floor plan, together with a magnetic fingerprint map of unmapped buildings using crowdsourced smartphone data. For floor plan construction, our system combines the use of dead reckoning technology, an observation model with geomagnetic signals, and trajectory fusion based on an affinity propagation algorithm. To obtain the indoor paths, the magnetic trajectory data obtained through crowdsourcing were first clustered using dynamic time warping similarity criteria. The trajectories were inferred from odometry tracing, and those belonging to the same cluster in the magnetic trajectory domain were then fused. Fusing these data effectively eliminates the inherent tracking errors originating from noisy sensors; as a result, we obtained highly accurate indoor paths. One advantage of our system is that no additional hardware such as a laser rangefinder or wheel encoder is required. Experimental results demonstrate that our proposed algorithm successfully constructs indoor floor plans with 0.48 m accuracy, which could benefit location-based services which lack indoor maps. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Local Homing Navigation Based on the Moment Model for Landmark Distribution and Features

by Changmin Lee and DaeEun Kim

Sensors 2017, 17(11), 2658; https://doi.org/10.3390/s17112658 - 17 Nov 2017

Cited by 7 | Viewed by 4287

Abstract

[-10]For local homing navigation, an agent is supposed to return home based on the surrounding environmental information. According to the snapshot model, the home snapshot and the current view are compared to determine the homing direction. In this paper, we propose a novel [...] Read more.

[-10]For local homing navigation, an agent is supposed to return home based on the surrounding environmental information. According to the snapshot model, the home snapshot and the current view are compared to determine the homing direction. In this paper, we propose a novel homing navigation method using the moment model. The suggested moment model also follows the snapshot theory to compare the home snapshot and the current view, but the moment model defines a moment of landmark inertia as the sum of the product of the feature of the landmark particle with the square of its distance. The method thus uses range values of landmarks in the surrounding view and the visual features. The center of the moment can be estimated as the reference point, which is the unique convergence point in the moment potential from any view. The homing vector can easily be extracted from the centers of the moment measured at the current position and the home location. The method effectively guides homing direction in real environments, as well as in the simulation environment. In this paper, we take a holistic approach to use all pixels in the panoramic image as landmarks and use the RGB color intensity for the visual features in the moment model in which a set of three moment functions is encoded to determine the homing vector. We also tested visual homing or the moment model with only visual features, but the suggested moment model with both the visual feature and the landmark distance shows superior performance. We demonstrate homing performance with various methods classified by the status of the feature, the distance and the coordinate alignment. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras

by Dewen Wu, Ruizhi Chen and Liang Chen

Sensors 2017, 17(11), 2645; https://doi.org/10.3390/s17112645 - 16 Nov 2017

Cited by 12 | Viewed by 5193

Abstract

Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a [...] Read more.

Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a visually well-defined object, e.g., a door, based on their visual observations. Can a smartphone camera do a similar job when it points to an object? In this paper, a visual positioning solution was developed based on a single image captured from a smartphone camera pointing to a well-defined object. The smartphone camera simulates the process of human eyes for the purpose of relatively locating themselves against a well-defined object. Extensive experiments were conducted with five types of smartphones on three different indoor settings, including a meeting room, a library, and a reading room. Experimental results shown that the average positioning accuracy of the solution based on five smartphone cameras is 30.6 cm, while that for the human-observed solution with 300 samples from 10 different people is 73.1 cm. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

I-DWRL: Improved Dual Wireless Radio Localization Using Magnetometer

by Abdul Aziz, Ramesh Kumar and Inwhee Joe

Sensors 2017, 17(11), 2630; https://doi.org/10.3390/s17112630 - 15 Nov 2017

Cited by 1 | Viewed by 3745

Abstract

In the dual wireless radio localization (DWRL) technique each sensor node is equipped with two ultra-wide band (UWB) radios; the distance between the two radios is a few tens of centimeters. For localization, the DWRL technique must use at least two pre-localized nodes [...] Read more.

In the dual wireless radio localization (DWRL) technique each sensor node is equipped with two ultra-wide band (UWB) radios; the distance between the two radios is a few tens of centimeters. For localization, the DWRL technique must use at least two pre-localized nodes to fully localize an unlocalized node. Moreover, in the DWRL technique it is also not possible for two sensor nodes to properly communicate location information unless each of the four UWB radios of two communicating sensor nodes cannot approach the remaining three radios. In this paper, we propose an improved DWRL (I-DWRL) algorithm along with mounting a magnetometer sensor on one of the UWB radios of all sensor nodes. This addition of a magnetometer helps to improve DWRL algorithm such that only one localized sensor node is required for the localization of an unlocalized sensor node, and localization can also be achieved even when some of the four radios of two nodes are unable to communicate with the remaining three radios. The results show that with the use of a magnetometer a greater number of nodes can be localized with a smaller transmission range, less energy and a shorter period of time. In comparison with the conventional DWRL algorithm, our I-DWRL not only maintains the localization error but also requires around half of semi-localizations, 60% of the time, 70% of the energy and a shorter communication range to fully localize an entire network. Moreover, I-DWRL can even localize more nodes while transmission range is not sufficient for DWRL algorithm. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

by Jun Qi and Guo-Ping Liu

Sensors 2017, 17(11), 2554; https://doi.org/10.3390/s17112554 - 6 Nov 2017

Cited by 110 | Viewed by 11774

Abstract

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the [...] Read more.

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach

0.61 mm

and

0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of

10.2

mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Comparison of Measurement Models for 3D Magnetic Localization and Tracking

by Guido De Angelis, Alessio De Angelis, Antonio Moschitta and Paolo Carbone

Sensors 2017, 17(11), 2527; https://doi.org/10.3390/s17112527 - 3 Nov 2017

Cited by 33 | Viewed by 5262

Abstract

In this paper, we consider magnetic positioning and tracking of objects and provide a comparison of the characteristics of two major measurement models: the magnetic dipole model and the mutual inductance model. The numerical results obtained by applying these models to a short-range [...] Read more.

In this paper, we consider magnetic positioning and tracking of objects and provide a comparison of the characteristics of two major measurement models: the magnetic dipole model and the mutual inductance model. The numerical results obtained by applying these models to a short-range position measurement application, with a maximum operating distance of approximately 50 cm, are compared. Based on the results of this comparison, a prototype 9-sensor array is developed, experimental tests are performed, and extensive measurement results are presented. Outcomes show the feasibility of tracking the position and orientation of a mobile coil in real time with a median positioning error below 1 cm and a worst-case error of about 2 cm and 11 degrees inside a spatial region of 30 × 30 × 30 cm³ operational volume. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Local Positioning System Using Flickering Infrared LEDs

by Thibaut Raharijaona, Rodolphe Mawonou, Thanh Vu Nguyen, Fabien Colonnier, Marc Boyron, Julien Diperi and Stéphane Viollet

Sensors 2017, 17(11), 2518; https://doi.org/10.3390/s17112518 - 3 Nov 2017

Cited by 38 | Viewed by 10857

Abstract

A minimalistic optical sensing device for the indoor localization is proposed to estimate the relative position between the sensor and active markers using amplitude modulated infrared light. The innovative insect-based sensor can measure azimuth and elevation angles with respect to two small and [...] Read more.

A minimalistic optical sensing device for the indoor localization is proposed to estimate the relative position between the sensor and active markers using amplitude modulated infrared light. The innovative insect-based sensor can measure azimuth and elevation angles with respect to two small and cheap active infrared light emitting diodes (LEDs) flickering at two different frequencies. In comparison to a previous lensless visual sensor that we proposed for proximal localization (less than 30 cm), we implemented: (i) a minimalistic sensor in terms of small size (10 cm

^{3}

), light weight (6 g) and low power consumption (0.4 W); (ii) an Arduino-compatible demodulator for fast analog signal processing requiring low computational resources; and (iii) an indoor positioning system for a mobile robotic application. Our results confirmed that the proposed sensor was able to estimate the position at a distance of 2 m with an accuracy as small as 2-cm at a sampling frequency of 100 Hz. Our sensor can be also suitable to be implemented in a position feedback loop for indoor robotic applications in GPS-denied environment. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Indoor Multipath Assisted Angle of Arrival Localization

by Stijn Wielandt and Lieven De Strycker

Sensors 2017, 17(11), 2522; https://doi.org/10.3390/s17112522 - 2 Nov 2017

Cited by 72 | Viewed by 8760

Abstract

Indoor radio frequency positioning systems enable a broad range of location aware applications. However, the localization accuracy is often impaired by Non-Line-Of-Sight (NLOS) connections and indoor multipath effects. An interesting evolution in widely deployed communication systems is the transition to multi-antenna devices with [...] Read more.

Indoor radio frequency positioning systems enable a broad range of location aware applications. However, the localization accuracy is often impaired by Non-Line-Of-Sight (NLOS) connections and indoor multipath effects. An interesting evolution in widely deployed communication systems is the transition to multi-antenna devices with beamforming capabilities. These properties form an opportunity for localization methods based on Angle of Arrival (AoA) estimation. This work investigates how multipath propagation can be exploited to enhance the accuracy of AoA localization systems. The presented multipath assisted method resembles a fingerprinting approach, matching an AoA measurement vector to a set of reference vectors. However, reference data is not generated by labor intensive site surveying. Instead, a ray tracer is used, relying on a-priori known floor plan information. The resulting algorithm requires only one fixed receiving antenna array to determine the position of a mobile transmitter in a room. The approach is experimentally evaluated in LOS and NLOS conditions, providing insights in the accuracy and robustness. The measurements are performed in various indoor environments with different hardware configurations. This leads to the conclusion that the proposed system yields a considerable accuracy improvement over common narrowband AoA positioning methods, as well as a reduction of setup efforts in comparison to conventional fingerprinting systems. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons

by F. Serhan Daniş and Ali Taylan Cemgil

Sensors 2017, 17(11), 2484; https://doi.org/10.3390/s17112484 - 29 Oct 2017

Cited by 75 | Viewed by 8688

Abstract

We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of [...] Read more.

We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

by Jaime Duque Domingo, Carlos Cerrada, Enrique Valero and Jose A. Cerrada

Sensors 2017, 17(10), 2391; https://doi.org/10.3390/s17102391 - 20 Oct 2017

Cited by 24 | Viewed by 13403

Abstract

This work presents an Indoor Positioning System to estimate the location of people navigating in complex indoor environments. The developed technique combines WiFi Positioning Systems and depth maps, delivering promising results in complex inhabited environments, consisting of various connected rooms, where people [...] Read more.

This work presents an Indoor Positioning System to estimate the location of people navigating in complex indoor environments. The developed technique combines WiFi Positioning Systems and depth maps, delivering promising results in complex inhabited environments, consisting of various connected rooms, where people are freely moving. This is a non-intrusive system in which personal information about subjects is not needed and, although RGB-D cameras are installed in the sensing area, users are only required to carry their smart-phones. In this article, the methods developed to combine the above-mentioned technologies and the experiments performed to test the system are detailed. The obtained results show a significant improvement in terms of accuracy and performance with respect to previous WiFi-based solutions as well as an extension in the range of operation. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

by Francesco Potortì, Sangjoon Park, Antonio Ramón Jiménez Ruiz, Paolo Barsocchi, Michele Girolami, Antonino Crivello, So Yeon Lee, Jae Hyun Lim, Joaquín Torres-Sospedra, Fernando Seco, Raul Montoliu, Germán Martin Mendoza-Silva, Maria Del Carmen Pérez Rubio, Cristina Losada-Gutiérrez, Felipe Espinosa and Javier Macias-Guarasa

Sensors 2017, 17(10), 2327; https://doi.org/10.3390/s17102327 - 13 Oct 2017

Cited by 72 | Viewed by 9878

Abstract

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living [...] Read more.

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space

by Chase Q. Wu and Li Wang

Sensors 2017, 17(10), 2304; https://doi.org/10.3390/s17102304 - 10 Oct 2017

Cited by 13 | Viewed by 4373

Abstract

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set [...] Read more.

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

by Mary B. Alatise and Gerhard P. Hancke

Sensors 2017, 17(10), 2164; https://doi.org/10.3390/s17102164 - 21 Sep 2017

Cited by 157 | Viewed by 20140

Abstract

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a [...] Read more.

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs). Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A Novel Energy-Efficient Approach for Human Activity Recognition

by Lingxiang Zheng, Dihong Wu, Xiaoyang Ruan, Shaolin Weng, Ao Peng, Biyu Tang, Hai Lu, Haibin Shi and Huiru Zheng

Sensors 2017, 17(9), 2064; https://doi.org/10.3390/s17092064 - 8 Sep 2017

Cited by 34 | Viewed by 5586

Abstract

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support [...] Read more.

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Landmark-Based Homing Navigation Using Omnidirectional Depth Information

by Changmin Lee, Seung-Eun Yu and DaeEun Kim

Sensors 2017, 17(8), 1928; https://doi.org/10.3390/s17081928 - 22 Aug 2017

Cited by 17 | Viewed by 6001

Abstract

A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in [...] Read more.

A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements

by André G. Ferreira, Duarte Fernandes, André P. Catarino and João L. Monteiro

Sensors 2017, 17(8), 1915; https://doi.org/10.3390/s17081915 - 19 Aug 2017

Cited by 46 | Viewed by 6145

Abstract

Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available [...] Read more.

Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available for the localization process, ultra-wide band (UWB) is a promising technology for such systems due to its robust signaling in harsh environments, through-wall propagation and high-resolution ranging. However, during emergency responders’ missions, the availability of UWB signals is generally low (the nodes have to be deployed as the emergency responders enter a building) and can be affected by the non-line-of-sight (NLOS) conditions. In this paper, the performance of four typical distance-based positioning algorithms (Analytical, Least Squares, Taylor Series, and Extended Kalman Filter methods) with only three ranging measurements is assessed based on a COTS UWB transceiver. These algorithms are compared based on accuracy, precision and root mean square error (RMSE). The algorithms were evaluated under two environments with different propagation conditions (an atrium and a lab), for static and mobile devices, and under the human body’s influence. A NLOS identification and error mitigation algorithm was also used to improve the ranging measurements. The results show that the Extended Kalman Filter outperforms the other algorithms in almost every scenario, but it is affected by the low measurement rate of the UWB system. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Hitchhiking Robots: A Collaborative Approach for Efficient Multi-Robot Navigation in Indoor Environments

by Abhijeet Ravankar, Ankit A. Ravankar, Yukinori Kobayashi and Takanori Emaru

Sensors 2017, 17(8), 1878; https://doi.org/10.3390/s17081878 - 15 Aug 2017

Cited by 22 | Viewed by 6431

Abstract

Hitchhiking is a means of transportation gained by asking other people for a (free) ride. We developed a multi-robot system which is the first of its kind to incorporate hitchhiking in robotics, and discuss its advantages. Our method allows the hitchhiker robot to [...] Read more.

Hitchhiking is a means of transportation gained by asking other people for a (free) ride. We developed a multi-robot system which is the first of its kind to incorporate hitchhiking in robotics, and discuss its advantages. Our method allows the hitchhiker robot to skip redundant computations in navigation like path planning, localization, obstacle avoidance, and map update by completely relying on the driver robot. This allows the hitchhiker robot, which performs only visual servoing, to save computation while navigating on the common path with the driver robot. The driver robot, in the proposed system performs all the heavy computations in navigation and updates the hitchhiker about the current localized positions and new obstacle positions in the map. The proposed system is robust to recover from `driver-lost’ scenario which occurs due to visual servoing failure. We demonstrate robot hitchhiking in real environments considering factors like service-time and task priority with different start and goal configurations of the driver and hitchhiker robots. We also discuss the admissible characteristics of the hitchhiker, when hitchhiking should be allowed and when not, through experimental results. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization

by Leonardo A. V. Souto, André Castro, Luiz Marcos Garcia Gonçalves and Tiago P. Nascimento

Sensors 2017, 17(8), 1824; https://doi.org/10.3390/s17081824 - 8 Aug 2017

Cited by 23 | Viewed by 10917

Abstract

Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach [...] Read more.

Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach for the identification and recognition of natural marks included in the environment using images from RGB-D (Red, Green, Blue, Depth) sensors. In the identification step, a structural analysis of the natural landmarks that are present in the environment is performed. The extraction of edge points of these landmarks is done using the 3D point cloud obtained from the RGB-D sensor. These edge points are smoothed through the

S l_{0}

algorithm, which minimizes the standard deviation of the normals at each point. Then, the second step of the proposed algorithm begins, which is the proper recognition of the natural landmarks. This recognition step is done as a real-time algorithm that extracts the points referring to the filtered edges and determines to which structure they belong to in the current scenario: stairs or doors. Finally, the geometrical characteristics that are intrinsic to the doors and stairs are identified. The approach proposed here has been validated with real robot experiments. The performed tests verify the efficacy of our proposed approach. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

by Iyad Husni Alshami, Noor Azurati Ahmad, Shamsul Sahibuddin and Firdaus Firdaus

Sensors 2017, 17(8), 1789; https://doi.org/10.3390/s17081789 - 5 Aug 2017

Cited by 45 | Viewed by 6336

Abstract

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints [...] Read more.

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

An Acquisition Scheme Based on a Matched Filter for Novel Communication and Navigation Fusion Signals

by Zhongliang Deng, Jun Mo, Buyun Jia and Xinmei Bian

Sensors 2017, 17(8), 1766; https://doi.org/10.3390/s17081766 - 2 Aug 2017

Cited by 17 | Viewed by 4918

Abstract

In order to enhance the positioning capability of terrestrial networks, a novel communication and navigation fusion signal is proposed. The novel signal multiplexes the communication and navigation signal in the same frequency band, and the navigation system is superimposed on the original communication [...] Read more.

In order to enhance the positioning capability of terrestrial networks, a novel communication and navigation fusion signal is proposed. The novel signal multiplexes the communication and navigation signal in the same frequency band, and the navigation system is superimposed on the original communication system. However, the application of pseudorandom noise (PRN) sequences in the navigation system is limited by the communication clock period. Taking the application of PRN sequences limited by the clock period as objects, the present study analyzes truncated PRN (TPRN) sequences. PRN sequences with a TPRN sequence as the navigation signal can overcome the communication system clock period limitation. Then, a matched filter algorithm with double detection (MFADD) is proposed to acquire the novel signal. The matched filter method is applied to the proposed algorithm to determine the start code phase of TPRN. Monte Carlo simulations and real data tests demonstrate the effectiveness of the proposed algorithm for the designed signal. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

by Óscar García, Javier Prieto, Ricardo S. Alonso and Juan M. Corchado

Sensors 2017, 17(8), 1749; https://doi.org/10.3390/s17081749 - 31 Jul 2017

Cited by 30 | Viewed by 8484

Abstract

Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns [...] Read more.

Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system’s real time and historical localization, tracking and contextual data, based on which customized recommendations are generated. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors

by Xiaomu Luo, Qiuju Guan, Huoyuan Tan, Liwen Gao, Zhengfei Wang and Xiaoyan Luo

Sensors 2017, 17(8), 1738; https://doi.org/10.3390/s17081738 - 29 Jul 2017

Cited by 60 | Viewed by 6625

Abstract

Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist [...] Read more.

Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks

by Juan J. Pérez-Solano, Jose M. Claver and Santiago Ezpeleta

Sensors 2017, 17(7), 1582; https://doi.org/10.3390/s17071582 - 6 Jul 2017

Cited by 5 | Viewed by 4928

Abstract

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, [...] Read more.

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Landmark-Based Drift Compensation Algorithm for Inertial Pedestrian Navigation

by Estefania Munoz Diaz, Maria Caamano and Francisco Javier Fuentes Sánchez

Sensors 2017, 17(7), 1555; https://doi.org/10.3390/s17071555 - 3 Jul 2017

Cited by 11 | Viewed by 4790

Abstract

The navigation of pedestrians based on inertial sensors, i.e., accelerometers and gyroscopes, has experienced a great growth over the last years. However, the noise of medium- and low-cost sensors causes a high error in the orientation estimation, particularly in the yaw angle. This [...] Read more.

The navigation of pedestrians based on inertial sensors, i.e., accelerometers and gyroscopes, has experienced a great growth over the last years. However, the noise of medium- and low-cost sensors causes a high error in the orientation estimation, particularly in the yaw angle. This error, called drift, is due to the bias of the z-axis gyroscope and other slow changing errors, such as temperature variations. We propose a seamless landmark-based drift compensation algorithm that only uses inertial measurements. The proposed algorithm adds a great value to the state of the art, because the vast majority of the drift elimination algorithms apply corrections to the estimated position, but not to the yaw angle estimation. Instead, the presented algorithm computes the drift value and uses it to prevent yaw errors and therefore position errors. In order to achieve this goal, a detector of landmarks, i.e., corners and stairs, and an association algorithm have been developed. The results of the experiments show that it is possible to reliably detect corners and stairs using only inertial measurements eliminating the need that the user takes any action, e.g., pressing a button. Associations between re-visited landmarks are successfully made taking into account the uncertainty of the position. After that, the drift is computed out of all associations and used during a post-processing stage to obtain a low-drifted yaw angle estimation, that leads to successfully drift compensated trajectories. The proposed algorithm has been tested with quasi-error-free turn rate measurements introducing known biases and with medium-cost gyroscopes in 3D indoor and outdoor scenarios. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A Sequential Optimization Calibration Algorithm for Near-Field Source Localization

by Jingjing Li, Xianxiang Yu and Guolong Cui

Sensors 2017, 17(6), 1405; https://doi.org/10.3390/s17061405 - 15 Jun 2017

Cited by 2 | Viewed by 3872

Abstract

This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations [...] Read more.

This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations of calibration sources involving the ranges and the azimuths by exploiting some imprecise a-priori knowledge of calibration sources. At each iteration of the proposed method, the source locations, and the gain and phase errors are obtained iteratively. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations. Results show that the proposed algorithm shares the capability to jointly estimate the source locations and the errors. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips

by Xiaolong Li, Yan Zheng, Jun Cai and Yunfei Yi

Sensors 2017, 17(6), 1391; https://doi.org/10.3390/s17061391 - 15 Jun 2017

Cited by 3 | Viewed by 4448

Abstract

This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level [...] Read more.

This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level in operation, which give rise to many practical challenges for designing localization algorithms. In order to address these challenges, we exploit the Markov theory to construct a

(l + 1) \times (l + 1)

-sized state transition matrix to remove the fluctuation, and then propose a priority-based pattern matching algorithm to search for the most similar match in the signal map to estimate the real position of unknown nodes. The experimental results show that, compared to two existing wireless ILSs, LANDMARC and SAIL, which have meter level positioning accuracy, the proposed TrackCC can achieve the decimeter level accuracy on average in both line-of-sight (LOS) and non-line-of-sight (NLOS) senarios. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms

by Manuel Castillo-Cara, Jesús Lovón-Melgarejo, Gusseppe Bravo-Rocca, Luis Orozco-Barbosa and Ismael García-Varea

Sensors 2017, 17(6), 1318; https://doi.org/10.3390/s17061318 - 7 Jun 2017

Cited by 40 | Viewed by 9008

Abstract

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have [...] Read more.

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks

by Junping Qin, Shiwen Sun, Qingxu Deng, Limin Liu and Yonghong Tian

Sensors 2017, 17(6), 1275; https://doi.org/10.3390/s17061275 - 2 Jun 2017

Cited by 9 | Viewed by 5258

Abstract

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal [...] Read more.

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator (RSSI) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object’s trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications

by Chunyang Yu, Haiyu Lan, Fuqiang Gu, Fei Yu and Naser El-Sheimy

Sensors 2017, 17(6), 1272; https://doi.org/10.3390/s17061272 - 2 Jun 2017

Cited by 39 | Viewed by 5941

Abstract

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) [...] Read more.

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification

by G. Ajay Kumar, Ashok Kumar Patil, Rekha Patil, Seong Sill Park and Young Ho Chai

Sensors 2017, 17(6), 1268; https://doi.org/10.3390/s17061268 - 2 Jun 2017

Cited by 135 | Viewed by 20175

Abstract

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing [...] Read more.

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks

by Jun Yan, Kegen Yu, Ruizhi Chen and Liang Chen

Sensors 2017, 17(6), 1246; https://doi.org/10.3390/s17061246 - 30 May 2017

Cited by 16 | Viewed by 4761

Abstract

In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, [...] Read more.

In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Vital Sign Monitoring and Mobile Phone Usage Detection Using IR-UWB Radar for Intended Use in Car Crash Prevention

by Seong Kyu Leem, Faheem Khan and Sung Ho Cho

Sensors 2017, 17(6), 1240; https://doi.org/10.3390/s17061240 - 30 May 2017

Cited by 89 | Viewed by 15026

Abstract

In order to avoid car crashes, active safety systems are becoming more and more important. Many crashes are caused due to driver drowsiness or mobile phone usage. Detecting the drowsiness of the driver is very important for the safety of a car. Monitoring [...] Read more.

In order to avoid car crashes, active safety systems are becoming more and more important. Many crashes are caused due to driver drowsiness or mobile phone usage. Detecting the drowsiness of the driver is very important for the safety of a car. Monitoring of vital signs such as respiration rate and heart rate is important to determine the occurrence of driver drowsiness. In this paper, robust vital signs monitoring through impulse radio ultra-wideband (IR-UWB) radar is discussed. We propose a new algorithm that can estimate the vital signs even if there is motion caused by the driving activities. We analyzed the whole fast time vital detection region and found the signals at those fast time locations that have useful information related to the vital signals. We segmented those signals into sub-signals and then constructed the desired vital signal using the correlation method. In this way, the vital signs of the driver can be monitored noninvasively, which can be used by researchers to detect the drowsiness of the driver which is related to the vital signs i.e., respiration and heart rate. In addition, texting on a mobile phone during driving may cause visual, manual or cognitive distraction of the driver. In order to reduce accidents caused by a distracted driver, we proposed an algorithm that can detect perfectly a driver's mobile phone usage even if there are various motions of the driver in the car or changes in background objects. These novel techniques, which monitor vital signs associated with drowsiness and detect phone usage before a driver makes a mistake, may be very helpful in developing techniques for preventing a car crash. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning

by Muhammad Usman Ali, Soojung Hur and Yongwan Park

Sensors 2017, 17(6), 1213; https://doi.org/10.3390/s17061213 - 25 May 2017

Cited by 34 | Viewed by 5823

Abstract

Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting [...] Read more.

Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting solutions require extensive and error-free surveys of environments to build radio-map databases, which play a key role in position estimation. Fingerprinting also requires random updates of the database, when there are significant changes in the environment or a decrease in the accuracy. The calibration of the fingerprinting database is a time-consuming and laborious effort that prevents the extensive adoption of this technique. In this paper, we present a systematic LOCALIzation approach, “LOCALI”, for indoor positioning, which does not require a calibration database and extensive updates. The LOCALI exploits the floor plan/wall map of the environment to estimate the target position by generating radio maps by integrating path-losses over certain trajectories in complex indoor environments, where triangulation using time information or the received signal strength level is highly erroneous due to the fading effects caused by multi-path propagation or absorption by environmental elements or varying antenna alignment. Experimental results demonstrate that by using the map information and environmental parameters, a significant level of accuracy in indoor positioning can be achieved. Moreover, this process requires considerably lesser effort compared to the calibration-based techniques. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

A Novel Real-Time Reference Key Frame Scan Matching Method

by Haytham Mohamed, Adel Moussa, Mohamed Elhabiby, Naser El-Sheimy and Abu Sesay

Sensors 2017, 17(5), 1060; https://doi.org/10.3390/s17051060 - 7 May 2017

Cited by 14 | Viewed by 5999

Abstract

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global [...] Read more.

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessArticle

Orthogonal Chirp-Based Ultrasonic Positioning

by Mohammad Omar Khyam, Shuzhi Sam Ge, Xinde Li and Mark Pickering

Sensors 2017, 17(5), 976; https://doi.org/10.3390/s17050976 - 27 Apr 2017

Cited by 13 | Viewed by 6272

Abstract

This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed [...] Read more.

This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed multiple access scheme is to utilize the oversampling methodology of orthogonal frequency-division multiplexing (OFDM) modulation and orthogonality of the discrete frequency components of a chirp waveform. In addition, the proposed orthogonal chirp waveforms also have all the advantages of a classical chirp waveform. Firstly, the performance of the waveforms is investigated through correlation analysis and then, in an indoor environment, evaluated through simulations and experiments for ultrasonic (US) positioning. For an operational range of approximately 1000 mm, the positioning root-mean-square-errors (RMSEs) &90% error were 4.54 mm and 6.68 mm respectively. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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1016 KiB

Open AccessArticle

Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

by Zhenghuan Wang, Heng Liu, Shengxin Xu, Xiangyuan Bu and Jianping An

Sensors 2017, 17(5), 969; https://doi.org/10.3390/s17050969 - 27 Apr 2017

Cited by 23 | Viewed by 4744

Abstract

Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is [...] Read more.

Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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2355 KiB

Open AccessArticle

Platform Architecture for Decentralized Positioning Systems

by Zakaria Kasmi, Abdelmoumen Norrdine and Jörg Blankenbach

Sensors 2017, 17(5), 957; https://doi.org/10.3390/s17050957 - 26 Apr 2017

Cited by 8 | Viewed by 6359

Abstract

A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station [...] Read more.

A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station and avoids the communication with a central unit such as a server or a base station. In addition, the calculation of the position on low-cost and resource-constrained devices presents a challenge due to the limited computing, storage capacity, as well as power supply. Therefore, we propose a platform architecture that enables the design of a system with the reusability of the components, extensibility (e.g., with other positioning technologies) and interoperability. Furthermore, the position is computed on a low-cost device such as a microcontroller, which simultaneously performs additional tasks such as data collecting or preprocessing based on an operating system. The platform architecture is designed, implemented and evaluated on the basis of two positioning systems: a field strength system and a time of arrival-based positioning system. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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1204 KiB

Open AccessArticle

An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

by Jenny Röbesaat, Peilin Zhang, Mohamed Abdelaal and Oliver Theel

Sensors 2017, 17(5), 951; https://doi.org/10.3390/s17050951 - 26 Apr 2017

Cited by 122 | Viewed by 11604

Abstract

Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based [...] Read more.

Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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3218 KiB

Open AccessArticle

Using Dimensionality Reduction Techniques for Refining Passive Indoor Positioning Systems Based on Radio Fingerprinting

by Pedro E. Lopez-de-Teruel, Oscar Canovas and Felix J. Garcia

Sensors 2017, 17(4), 871; https://doi.org/10.3390/s17040871 - 15 Apr 2017

Cited by 13 | Viewed by 5006

Abstract

Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it [...] Read more.

Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it is difficult to verify and visualize that separability since radio maps are constituted by multi-dimensional observations whose dimension is directly related to the number of access points or monitors being employed for localization purposes. In this paper, we propose a refinement cycle for passive indoor positioning systems, which is based on dimensionality reduction techniques, to evaluate the quality of a radio map. By means of these techniques and our own data representation, we have defined two different visualization methods to obtain graphical information about the quality of a particular radio map in terms of overlapping areas and outliers. That information will be useful to determine whether new monitors are required or some existing ones should be moved. We have performed an exhaustive experimental analysis based on a variety of different scenarios, some deployed by our own research group and others corresponding to a well-known existing dataset widely analyzed by the community, in order to validate our proposal. As we will show, among the different combinations of data representation methods and dimensionality reduction techniques that we discuss, we have found that there are some specific configurations that are more useful in order to perform the refinement process. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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4456 KiB

Open AccessArticle

Modeling Infrared Signal Reflections to Characterize Indoor Multipath Propagation

by Álvaro De-La-Llana-Calvo, José Luis Lázaro-Galilea, Alfredo Gardel-Vicente, David Rodríguez-Navarro, Ignacio Bravo-Muñoz, Georgios Tsirigotis and Juan Iglesias-Miguel

Sensors 2017, 17(4), 847; https://doi.org/10.3390/s17040847 - 13 Apr 2017

Cited by 16 | Viewed by 5291

Abstract

In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR [...] Read more.

In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR signals (IR-LPS) to evaluate the behavior of transmitted signal Multipaths (MP), which are the main cause of error in IR-LPS, and makes several contributions to mitigation methods. Current methods are based on physics, optics, geometry and empirical methods, but these do not meet our requirements because of the need to apply several different restrictions and employ complex tools. We propose a simplified model based on only two reflection components, together with a method for determining the model parameters based on 12 empirical measurements that are easily performed in the real environment where the IR-LPS is being applied. Our experimental results show that the model provides a comprehensive solution to the real behavior of IR MP, yielding small errors when comparing real and modeled data (the mean error ranges from 1% to 4% depending on the environment surface materials). Other state-of-the-art methods yielded mean errors ranging from 15% to 40% in test measurements. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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7129 KiB

Open AccessArticle

Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar

by Faheem Khan, Seong Kyu Leem and Sung Ho Cho

Sensors 2017, 17(4), 833; https://doi.org/10.3390/s17040833 - 11 Apr 2017

Cited by 76 | Viewed by 12980

Abstract

Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the [...] Read more.

Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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1286 KiB

Open AccessArticle

A Novel Robust Trilateration Method Applied to Ultra-Wide Bandwidth Location Systems

by Jiahong Li, Xianghu Yue, Jie Chen and Fang Deng

Sensors 2017, 17(4), 795; https://doi.org/10.3390/s17040795 - 7 Apr 2017

Cited by 41 | Viewed by 5475

Abstract

Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the [...] Read more.

Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the range of the circle within a certain confidence interval is proposed. In the method, the confidence interval is estimated based on the Cramér–Rao lower bound of the time of flight (TOF) measurement. Furthermore, an intersection determination method is proposed to select the intersection point with higher confidence level. The simulation and experimental results show the superiority of the proposed method in localization accuracy and robustness to noise compared to the conventional trilateration method, e.g., the centroid-based and least squares based trilateration methods. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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8788 KiB

Open AccessArticle

An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks

by Marco Passafiume, Stefano Maddio and Alessandro Cidronali

Sensors 2017, 17(4), 717; https://doi.org/10.3390/s17040717 - 29 Mar 2017

Cited by 42 | Viewed by 8891

Abstract

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where [...] Read more.

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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4757 KiB

Open AccessArticle

Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring

by José M. Alcalá, Jesús Ureña, Álvaro Hernández and David Gualda

Sensors 2017, 17(2), 351; https://doi.org/10.3390/s17020351 - 11 Feb 2017

Cited by 51 | Viewed by 7347

Abstract

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New [...] Read more.

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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2118 KiB

Open AccessArticle

Reliable Identiﬁcation of Vehicle-Boarding Actions Based on Fuzzy Inference Syste

by DaeHan Ahn, Homin Park, Seokhyun Hwang and Taejoon Park

Sensors 2017, 17(2), 333; https://doi.org/10.3390/s17020333 - 9 Feb 2017

Cited by 11 | Viewed by 4724

Abstract

Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a [...] Read more.

Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrancebyleveragingbuilt-insmartphonesensorsonly. Theresultsofourcomprehensiveevaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8 %accuracy regardless of smartphone positions and vehicle types. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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Open AccessProject Report

HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly

by Emanuele Frontoni, Rama Pollini, Paola Russo, Primo Zingaretti and Graziano Cerri

Sensors 2017, 17(11), 2610; https://doi.org/10.3390/s17112610 - 13 Nov 2017

Cited by 20 | Viewed by 6482

Abstract

The aim of this paper is to present the main results of HDOMO, an Ambient Assisted Living (AAL) project that involved 16 Small and Medium Enterprises (SMEs) and 2 research institutes. The objective of the project was to create an autonomous and automated [...] Read more.

The aim of this paper is to present the main results of HDOMO, an Ambient Assisted Living (AAL) project that involved 16 Small and Medium Enterprises (SMEs) and 2 research institutes. The objective of the project was to create an autonomous and automated domestic environment, primarily for elderly people and people with physical and motor disabilities. A known and familiar environment should help users in their daily activities and it should act as a virtual caregiver by calling, if necessary, relief efforts. Substantially, the aim of the project is to simplify the life of people in need of support, while keeping them autonomous in their private environment. From a technical point of view, the project provides the use of different Smart Objects (SOs), able to communicate among each other, in a cloud base infrastructure, and with the assisted users and their caregivers, in a perspective of interoperability and standardization of devices, usability and effectiveness of alarm systems. In the state of the art there are projects that achieve only a few of the elements listed. The HDOMO project aims to achieve all of them in one single project effectively. The experimental trials performed in a real scenario demonstrated the accuracy and efficiency of the system in extracting and processing data in real time to promptly acting, and in providing timely response to the needs of the user by integrating and confirming main alarms with different interoperable smart sensors. The article proposes a new technique to improve the accuracy of the system in detecting alarms using a multi-SO approach with information fusion between different devices, proving that this architecture can provide robust and reliable results on real environments. Full article

(This article belongs to the Special Issue Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring)

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