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4 pages, 3752 KiB

Open AccessEditorial

Advances in Indoor Positioning and Indoor Navigation

by Antoni Perez-Navarro, Raúl Montoliu and Joaquín Torres-Sospedra

Sensors 2022, 22(19), 7375; https://doi.org/10.3390/s22197375 - 28 Sep 2022

Cited by 2 | Viewed by 3026

Abstract

Locating devices in indoor environments has become a key issue for many emerging location-based applications and intelligent spaces in different fields [...] Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

Research

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29 pages, 27963 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Magnetic Field Measurements for Indoor Positioning

by Guanglie Ouyang and Karim Abed-Meraim

Sensors 2022, 22(11), 4014; https://doi.org/10.3390/s22114014 - 25 May 2022

Cited by 11 | Viewed by 3945

Abstract

Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic [...] Read more.

Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic field (MF) measurements from heterogeneous smartphones. It demonstrates that, in the absence of disturbances, the MF measurements in indoor environments follow a Gaussian distribution with temporal stability and spatial discernibility. It shows the fluctuations in magnetic field intensity caused by the rotation of a smartphone around the Z-axis. Secondly, it suggests that the RLOWESS method can be used to eliminate magnetic field anomalies, using magnetometer calibration to ensure consistent MF measurements in heterogeneous smartphones. Thirdly, it tests the magnetic field positioning performance of homogeneous and heterogeneous devices using different machine learning methods. Finally, it summarizes the feasibility/limitations of using only MF measurement for indoor positioning. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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19 pages, 5370 KiB

Open AccessArticle

Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis

by Dorian Harder, Hossein Shoushtari and Harald Sternberg

Sensors 2022, 22(9), 3289; https://doi.org/10.3390/s22093289 - 25 Apr 2022

Cited by 3 | Viewed by 2352

Abstract

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step [...] Read more.

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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27 pages, 104112 KiB

Open AccessArticle

Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi

by Ghazaleh Kia, Laura Ruotsalainen and Jukka Talvitie

Sensors 2022, 22(9), 3204; https://doi.org/10.3390/s22093204 - 21 Apr 2022

Cited by 11 | Viewed by 4704

Abstract

A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work [...] Read more.

A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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18 pages, 21115 KiB

Open AccessArticle

Real-Time Sonar Fusion for Layered Navigation Controller

by Wouter Jansen, Dennis Laurijssen and Jan Steckel

Sensors 2022, 22(9), 3109; https://doi.org/10.3390/s22093109 - 19 Apr 2022

Cited by 4 | Viewed by 2383

Abstract

Navigation in varied and dynamic indoor environments remains a complex task for autonomous mobile platforms. Especially when conditions worsen, typical sensor modalities may fail to operate optimally and subsequently provide inapt input for safe navigation control. In this study, we present an approach [...] Read more.

Navigation in varied and dynamic indoor environments remains a complex task for autonomous mobile platforms. Especially when conditions worsen, typical sensor modalities may fail to operate optimally and subsequently provide inapt input for safe navigation control. In this study, we present an approach for the navigation of a dynamic indoor environment with a mobile platform with a single or several sonar sensors using a layered control system. These sensors can operate in conditions such as rain, fog, dust, or dirt. The different control layers, such as collision avoidance and corridor following behavior, are activated based on acoustic flow queues in the fusion of the sonar images. The novelty of this work is allowing these sensors to be freely positioned on the mobile platform and providing the framework for designing the optimal navigational outcome based on a zoning system around the mobile platform. Presented in this paper is the acoustic flow model used, as well as the design of the layered controller. Next to validation in simulation, an implementation is presented and validated in a real office environment using a real mobile platform with one, two, or three sonar sensors in real time with 2D navigation. Multiple sensor layouts were validated in both the simulation and real experiments to demonstrate that the modular approach for the controller and sensor fusion works optimally. The results of this work show stable and safe navigation of indoor environments with dynamic objects. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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31 pages, 15792 KiB

Open AccessArticle

NIKE BLUETRACK: Blue Force Tracking in GNSS-Denied Environments Based on the Fusion of UWB, IMUs and 3D Models

by Karin Mascher, Markus Watzko, Axel Koppert, Julian Eder, Peter Hofer and Manfred Wieser

Sensors 2022, 22(8), 2982; https://doi.org/10.3390/s22082982 - 13 Apr 2022

Cited by 5 | Viewed by 3334

Abstract

Blue force tracking represents an essential task in the field of military applications. A blue force tracking system provides the location information of their own forces on a map to commanders. For the command post, this results in more efficient operation control with [...] Read more.

Blue force tracking represents an essential task in the field of military applications. A blue force tracking system provides the location information of their own forces on a map to commanders. For the command post, this results in more efficient operation control with increasing safety. In underground structures (e.g., tunnels or subways), the localisation is challenging due to the lack of GNSS signals. This paper presents a localisation system for military or emergency forces tailored to usage in complex underground structures. In a particle filter, position changes from a dual foot-mounted INS are fused with opportunistic UWB ranges and data from a 3D tunnel model to derive position information. A concept to deal with the absence of UWB infrastructure or 3D tunnel models is illustrated. Recurrent neural network methodologies are applied to cope with different motion types of the operators. The evaluation of the positioning algorithm took place in a street tunnel. If a fully installed infrastructure was available, positioning errors under one metre were reached. The results also showed that the INS can bridge UWB outages. A particle-filter-based approach to UWB anchor mapping is presented, and the first simulation results showed its viability. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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20 pages, 4125 KiB

Open AccessArticle

Accuracy and Precision of Agents Orientation in an Indoor Positioning System Using Multiple Infrastructure Lighting Spotlights and a PSD Sensor

by Álvaro De-La-Llana-Calvo, José Luis Lázaro-Galilea, Aitor Alcázar-Fernández, Alfredo Gardel-Vicente, Ignacio Bravo-Muñoz and Andreea Iamnitchi

Sensors 2022, 22(8), 2882; https://doi.org/10.3390/s22082882 - 9 Apr 2022

Cited by 7 | Viewed by 2794

Abstract

In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device [...] Read more.

In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device (PSD) sensors and the visible light emitted from the illumination of the room in which it is located. The orientation estimation will require that the PSD sensor receives signal from either 2 or 4 light sources simultaneously. As will be shown in the article, the error determining the rotation angle of the agent with the on-board sensor is less than 0.2 degrees for two emitters. On the other hand, by using 4 light sources the three Euler rotation angles are determined, with mean errors in the measurements smaller than 0.35° for the x- and y-axis and 0.16° for the z-axis. The accuracy of the measurement has been evaluated experimentally in a 2.5 m-high ceiling room over an area of 2.2 m

^{2}

using geodetic measurement tools to establish the reference ground truth values. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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19 pages, 4605 KiB

Open AccessArticle

Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin

by Jonas Ninnemann, Paul Schwarzbach, Michael Schultz and Oliver Michler

Sensors 2022, 22(8), 2859; https://doi.org/10.3390/s22082859 - 8 Apr 2022

Cited by 11 | Viewed by 2792

Abstract

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger [...] Read more.

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks. These multi-purpose communication systems serve a variety of capabilities, ranging from passenger convenience communication services, over crew member devices, to maintenance planning. In addition, radio-based sensing enables an efficient sensory basis for state monitoring; e.g., passive seat occupancy detection. Within the scope of the connected aircraft cabin, this article presents a multipath-assisted radio sensing (MARS) approach using the propagation information of transmitted signals, which are provided by the channel impulse response (CIR) of the wireless communication channel. By performing a geometrical mapping of the CIR, reflection sources are revealed, and the occupancy state can be derived. For this task, both probabilistic filtering and k-nearest neighbor classification are discussed. In order to evaluate the proposed methods, passenger occupancy detection and state detection for the future automation of passenger safety announcements and checks are addressed. Therefore, experimental measurements are performed using commercially available wideband communication devices, both in close to ideal conditions in an RF anechoic chamber and a cabin seat mockup. In both environments, a reliable radio sensing state detection was achieved. In conclusion, this paper provides a basis for the future integration of energy and spectrally efficient joint communication and sensing radio systems within the connected aircraft cabin. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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19 pages, 3910 KiB

Open AccessArticle

An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression

by Frida Viset, Rudy Helmons and Manon Kok

Sensors 2022, 22(8), 2833; https://doi.org/10.3390/s22082833 - 7 Apr 2022

Cited by 27 | Viewed by 5011

Abstract

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process [...] Read more.

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of

O (N_{p} N_{m}^{2})

, where

N_{p}

is the number of particles, and

N_{m}

is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of

O (N_{m}^{2})

. We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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37 pages, 1872 KiB

Open AccessArticle

Meaningful Test and Evaluation of Indoor Localization Systems in Semi-Controlled Environments

by Jakob Schyga, Johannes Hinckeldeyn and Jochen Kreutzfeldt

Sensors 2022, 22(7), 2797; https://doi.org/10.3390/s22072797 - 6 Apr 2022

Cited by 5 | Viewed by 3740

Abstract

Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature [...] Read more.

Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed—a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls, is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real-world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based light detection and ranging (LiDAR) ILS, an ultra wideband ILS, and a camera-based ILS for the application of automated guided vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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18 pages, 5971 KiB

Open AccessArticle

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal

by Aristotelis Koutris, Theodoros Siozos, Yannis Kopsinis, Aggelos Pikrakis, Timon Merk, Matthias Mahlig, Stylianos Papaharalabos and Peter Karlsson

Sensors 2022, 22(7), 2759; https://doi.org/10.3390/s22072759 - 2 Apr 2022

Cited by 29 | Viewed by 6908

Abstract

In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator [...] Read more.

In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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24 pages, 9920 KiB

Open AccessEditor’s ChoiceArticle

Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment

by Daniel Neunteufel, Stefan Grebien and Holger Arthaber

Sensors 2022, 22(7), 2663; https://doi.org/10.3390/s22072663 - 30 Mar 2022

Cited by 8 | Viewed by 2516

Abstract

The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time [...] Read more.

The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time of flight (ToF) measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates time difference of arrival (TDoA) and AoA measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on software-defined radio (SDR) platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ISM band at

2.4 GHz

. We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node RSS measurements using a simple clustering approach. In the considered scenario, an overall positioning root-mean-square error (RMSE) of

2.19 m

is achieved. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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18 pages, 1889 KiB

Open AccessArticle

Experimental Evaluation of IEEE 802.15.4z UWB Ranging Performance under Interference

by Janis Tiemann, Johannes Friedrich and Christian Wietfeld

Sensors 2022, 22(4), 1643; https://doi.org/10.3390/s22041643 - 19 Feb 2022

Cited by 15 | Viewed by 5783

Abstract

The rise of precise wireless localization for industrial and consumer use is continuing to challenge a significant amount of research. Recently the new ultra-wideband standard IEEE 802.15.4z was released to increase both the robustness and security of the underlying message exchanges. Due to [...] Read more.

The rise of precise wireless localization for industrial and consumer use is continuing to challenge a significant amount of research. Recently the new ultra-wideband standard IEEE 802.15.4z was released to increase both the robustness and security of the underlying message exchanges. Due to the lack of accessible transceivers, most of the current research on this is of theoretical nature though. This work provides the first experimental evaluation of the ranging performance in realistic environments and also assesses the robustness to different sources of interference. To evaluate the individual aspects, a set of three different experiments are conducted. One experiment with realistic movement and two additional with targeted interference. It could be shown that the cryptographic additions of the new standard can provide sufficient information to improve the reliability of the ranging results under multi-user interference significantly. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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19 pages, 5744 KiB

Open AccessArticle

Component-Wise Error Correction Method for UWB-Based Localization in Target-Following Mobile Robot

by Kyungbin Bae, Yooha Son, Young-Eun Song and Hoeryong Jung

Sensors 2022, 22(3), 1180; https://doi.org/10.3390/s22031180 - 4 Feb 2022

Cited by 7 | Viewed by 3195

Abstract

Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization [...] Read more.

Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization accuracy by compensating localization bias and high-frequency deviations component by component. Initial calibration method is proposed to measure the device-dependent localization bias, which enables a compensation of the bias error not only at the calibration points, but also at the any other points. An iterative complementary filter, which recursively produces optimal estimation for each timeframe as a weighted sum of previous and current estimation depending on the reliability of each estimation, is proposed to reduce the deviation of the localization error. The performance of the proposed method is validated using simulations and experiments. Both the magnitude and deviation of the localization error were significantly improved by up to 77 and 51%, respectively, compared with the previous method. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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39 pages, 6473 KiB

Open AccessArticle

Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization

by Grigorios G. Anagnostopoulos and Alexandros Kalousis

Sensors 2022, 22(3), 1088; https://doi.org/10.3390/s22031088 - 30 Jan 2022

Cited by 5 | Viewed by 3543

Abstract

Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The [...] Read more.

Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The concept of estimating, dynamically, the accuracy of the position estimates provided by an IPS has been sporadically studied in the literature of the field. Recently, this concept has started being studied as well in the context of outdoor positioning systems of Internet of Things (IoT) based on Low-Power Wide-Area Networks (LPWANs). What is problematic is that the consistent comparison of the proposed methods is quasi nonexistent: new methods rarely use previous ones as baselines; often, a small number of evaluation metrics are reported while different metrics are reported among different relevant publications, the use of open data is rare, and the publication of open code is absent. In this work, we present an open-source, reproducible benchmarking framework for evaluating and consistently comparing various methods of Dynamic Accuracy Estimation (DAE). This work reviews the relevant literature, presenting in a consistent terminology commonalities and differences and discussing baselines and evaluation metrics. Moreover, it evaluates multiple methods of DAE using open data, open code, and a rich set of relevant evaluation metrics. This is the first work aiming to establish the state of the art of methods of DAE determination in IPS and in LPWAN positioning systems, through an open, transparent, holistic, reproducible, and consistent evaluation of the methods proposed in the relevant literature. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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24 pages, 10862 KiB

Open AccessArticle

Simulation Tool and Online Demonstrator for CDMA-Based Ultrasonic Indoor Localization Systems

by María Carmen Pérez-Rubio, Álvaro Hernández, David Gualda-Gómez, Santiago Murano, Jorge Vicente-Ranera, Francisco Ciudad-Fernández, José Manuel Villadangos and Rubén Nieto

Sensors 2022, 22(3), 1038; https://doi.org/10.3390/s22031038 - 28 Jan 2022

Cited by 7 | Viewed by 3735

Abstract

This work presents the CODEUS platform, which includes a simulation tool together with an online experimental demonstrator to offer analysis and testing flexibility for researchers and developers in Ultrasonic Indoor Positioning Systems (UIPSs). The simulation platform allows most common encoding techniques and sequences [...] Read more.

This work presents the CODEUS platform, which includes a simulation tool together with an online experimental demonstrator to offer analysis and testing flexibility for researchers and developers in Ultrasonic Indoor Positioning Systems (UIPSs). The simulation platform allows most common encoding techniques and sequences to be tested in a configurable UIPS. It models the signal modulation and processing, the ultrasonic transducers’ response, the beacon distribution, the channel propagation effects, the synchronism, and the application of different positioning algorithms. CODEUS provides results and performance analysis for different metrics and at different stages of the signal processing. The UIPS simulation tool is specified by means of the MATLAB© App-Designer environment, which enables the definition of a user-friendly interface. It has also been linked to an online demonstrator that can be managed remotely by means of a website, thus avoiding any hardware requirement or equipment on behalf of researchers. This demonstrator allows the selected transmission schemes, modulation or encoding techniques to be validated in a real UIPS, therefore enabling a fast and easy way of carrying out experimental tests in a laboratory environment, while avoiding the time-consuming tasks related to electronic design and prototyping in the UIPS field. Both simulator and online demonstrator are freely available for researchers and students through the corresponding website. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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16 pages, 3058 KiB

Open AccessArticle

Using Perspective-n-Point Algorithms for a Local Positioning System Based on LEDs and a QADA Receiver

by Elena Aparicio-Esteve, Jesús Ureña, Álvaro Hernández, Daniel Pizarro and David Moltó

Sensors 2021, 21(19), 6537; https://doi.org/10.3390/s21196537 - 30 Sep 2021

Cited by 4 | Viewed by 2576

Abstract

The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to [...] Read more.

The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver. The receiver is based on a quadrant photodiode angular diversity aperture (QADA) plus an aperture placed over it. This configuration can be modelled as a perspective camera, where the image position of the transmitters can be used to recover the receiver’s 3D pose. This process is known as the perspective-n-point (PnP) problem, which is well known in computer vision and photogrammetry. This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space of 2 × 2 m

^{2}

based on four co-planar transmitters and with a distance from transmitters to receiver up to

3.4

m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver. In addition, correlation techniques (match filtering) are used to determine the image points projected from each emitter on the QADA. This work uses Monte Carlo simulations to characterize the absolute errors for a grid of test points under noisy measurements, as well as the robustness of the system when varying the 3D location of one transmitter. The IPPE algorithm obtained the best performance in this configuration. The proposal has also been experimentally evaluated in a real setup. The estimation of the receiver’s position at three particular points for roll angles of the receiver of

γ =

{0°, 120°, 210° and 300°} using the IPPE algorithm achieves average absolute errors and standard deviations of

4.33

cm,

3.51

cm and

28.90

cm; and

1.84

cm,

1.17

cm and

19.80

cm in the coordinates x, y and z, respectively. These positioning results are in line with those obtained in previous work using triangulation techniques but with the addition that the complete pose of the receiver (x, y, z,

α

,

β

,

γ

) is obtained in this proposal. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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23 pages, 5185 KiB

Open AccessArticle

Smartphone-Based Inertial Odometry for Blind Walkers

by Peng Ren, Fatemeh Elyasi and Roberto Manduchi

Sensors 2021, 21(12), 4033; https://doi.org/10.3390/s21124033 - 11 Jun 2021

Cited by 15 | Viewed by 3230

Abstract

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a [...] Read more.

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers. Full article

(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)

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