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Remote Sensing
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Indoor Localization
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Indoor Localization

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 22200

Special Issue Editor

Dr. Guido De Angelis

E-Mail Website
Guest Editor
Regione Umbria, 06128 Perugia, Italy
Interests: statistical signal processing; sensor fusion; detection and estimation theory; machine learning; positioning; traffic flow; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The topics of indoor localization, tracking, and mapping play an important role in modern daily life, due to the widespread diffusion of devices and technologies. While it can be envisioned that portable electronic devices will feature seamless outdoor/indoor positioning capabilities in the future, the current technology does not yet allow this with acceptable cost–performance tradeoffs.

A current trend in addressing indoor localization, tracking, and mapping is to use standard, low-cost, and already deployed technologies. These technologies are highly heterogeneous, encompassing inertial measurement units, magnetic field, ultrawideband, sonar, laser, IR, visual light communications, radiofrequencies, WiFi, magnetic fields, UWB, RFID, Bluetooth, NFC, 4GPP/LTE, 802.11x, digital TV, or what are generally referred to as the so-called available signals-of-opportunity.

Past research has usually addressed the design and implementation of data fusion methods using already available technologies or the realization of low-cost sensors. Data fusion in indoor localization, tracking, and mapping is a key element for further advances in this field and presents exciting challenges for signal processing practitioners and researchers.

Due to the large variety of technologies and standards involved, a data fusion algorithm typically needs to account for several technologies, such as batteries, system identification, communication channel models, filtering (e.g., Kalman and Bayesian filtering), machine learning, and many more.

The application fields can cover localization, healthcare, autonomous vehicles, passive localization systems, robotics, Internet of Things applications, automated guided vehicles in manufacturing lines, first responder navigation, vehicular navigation, asset navigation and tracking, indoor unmanned vehicles, or people-movers.

In this Special Issue of Remote Sensing, we are soliciting submissions of original works addressing the fundamentals, supporting technologies, and technical issues on data fusion of heterogeneous technologies for localization, tracking, and mapping. Note that the topics are not limited to only cover design and analysis.

This Special Issue of Remote Sensing aims to publish novel results on the most recent developments in data fusion for indoor localization, tracking, and mapping with emphasis on the integration of various technologies for improved performance. The topics include, but are not limited to:

  • Advanced simultaneous localization, tracking, and mapping
  • Advanced data fusion schemes for heterogeneous technologies
  • Environment applications
  • Healthcare applications
  • Cooperative Indoor positioning
  • Data Fusion System
  • Filtering
  • Indoor unmanned vehicles navigation
  • Passive localization system
  • System Identification
  • Localization methods for the Internet of Things
  • Hybrid IMU and magnetic pedestrian navigation
  • Cooperative Localization system
  • Passive and active RFID
  • Magnetic Positioning System.
  • Wireless sensor radar
  • Traffic flow analysis in Indoor Localization
  • Mobility models for tracking
Dr. Guido De Angelis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • indoor positioning
  • filtering; sensors
  • signal processing algorithms
  • ultra-wideband technology
  • filtering
  • cooperative positioning

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Published Papers (6 papers)

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14 pages, 8381 KiB  
Communication
L5IN: Overview of an Indoor Navigation Pilot Project
by Caroline Schuldt, Hossein Shoushtari, Nils Hellweg and Harald Sternberg
Remote Sens. 2021, 13(4), 624; https://doi.org/10.3390/rs13040624 - 9 Feb 2021
Cited by 16 | Viewed by 4255
Abstract
While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (L5IN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor [...] Read more.
While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (L5IN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor environments. The aim of this project is to use the new available 5G technology to show how navigation systems, which have thus far only been available in the outdoor segment, can now be integrated into existing smartphone systems for indoor navigation. This paper gives an overview of the project and presents the different work packages leading to a holistic approach towards the development of an indoor navigation application for pedestrians. By using a specific app with open interfaces, it is planned to make navigation possible in all buildings modeled according to certain standards. The challenge involved is that, unlike outdoor maps, there is no map basis for buildings. For this reason, different approaches to map generation were examined. In a building information model (BIM), all information will be collected and made available via a database for positioning and visualization. The focus is furthermore on positioning, achieved through smartphone sensors and 5G, so that users can orientate themselves in buildings without having to connect to singular systems. It shall be shown that positioning with a standard deviation of 2–3 m and a confidence interval of 68 % is possible. Another advantage of 5G, the ability to send real-time data in higher data packages, will be used for data transmission. The basic idea of 5G-based indoor navigation will be enabled with radio cells of the providers, which will be set up on the HafenCity University campus. The complex university building will be used as a prototype environment. Full article
(This article belongs to the Special Issue Indoor Localization)
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22 pages, 2268 KiB  
Article
A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking
by Ning Zhou, Lawrence Lau, Ruibin Bai and Terry Moore
Remote Sens. 2021, 13(1), 132; https://doi.org/10.3390/rs13010132 - 2 Jan 2021
Cited by 35 | Viewed by 3709
Abstract
In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. [...] Read more.
In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications. Full article
(This article belongs to the Special Issue Indoor Localization)
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22 pages, 1688 KiB  
Article
Efficient Marginalized Particle Smoother for Indoor CSS–TOF Localization with Non-Gaussian Errors
by Yuan Yang, Manyi Wang, Yunxia Qiao, Bo Zhang and Haoran Yang
Remote Sens. 2020, 12(22), 3838; https://doi.org/10.3390/rs12223838 - 23 Nov 2020
Cited by 3 | Viewed by 2449
Abstract
The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising [...] Read more.
The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising as involving the past, present, and future observations. However, the main problems are how to derive trackable smoothing recursions and to avoid the degeneracy of particle-based smoothed distributions. To incorporate the backward smoothing density propagation with the forward probability recursion efficiently, we propose a lightweight Marginalized Particle Smoother (MPS) for nonlinear and non-Gaussian errors mitigation. The performance of the position prediction, filtering, and smoothing are investigated in real-world experiments carried out with vehicle on-board sensors. Results demonstrate the proposed smoother enables a great tool by reducing temporal and spatial errors of mobile trajectories, with the cost of a few sequence delay and a small number of particles. Therefore, MPS outperforms the filtering and smoothing methods under weak assumptions, low computation, and memory requirements. In the view that the sampled trajectories stay numerically stable, the MPS form is validated to be applicable for time-series position tracking. Full article
(This article belongs to the Special Issue Indoor Localization)
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27 pages, 13016 KiB  
Article
Visual-Inertial Odometry of Smartphone under Manhattan World
by YuAn Wang, Liang Chen, Peng Wei and XiangChen Lu
Remote Sens. 2020, 12(22), 3818; https://doi.org/10.3390/rs12223818 - 20 Nov 2020
Cited by 7 | Viewed by 3751
Abstract
Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method [...] Read more.
Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry. Full article
(This article belongs to the Special Issue Indoor Localization)
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21 pages, 3620 KiB  
Article
Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point
by David Sánchez-Rodríguez, Miguel A. Quintana-Suárez, Itziar Alonso-González, Carlos Ley-Bosch and Javier J. Sánchez-Medina
Remote Sens. 2020, 12(12), 1995; https://doi.org/10.3390/rs12121995 - 21 Jun 2020
Cited by 11 | Viewed by 4372
Abstract
In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. [...] Read more.
In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point. Full article
(This article belongs to the Special Issue Indoor Localization)
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9 pages, 292 KiB  
Letter
Decision Support System Based on Indoor Location for Personnel Management
by Néstor Álvarez-Díaz and Pino Caballero-Gil
Remote Sens. 2021, 13(2), 248; https://doi.org/10.3390/rs13020248 - 13 Jan 2021
Cited by 2 | Viewed by 2169
Abstract
A wide variety of business areas organize their work based on the location of their employees because only by taking these locations into account, they can schedule activities properly. However, in a large number of cases, the requirement of immediacy, such as the [...] Read more.
A wide variety of business areas organize their work based on the location of their employees because only by taking these locations into account, they can schedule activities properly. However, in a large number of cases, the requirement of immediacy, such as the need to help an injured person in a hospital or to dry up water in a busy hallway to prevent people from slipping, is a major constraint. This work is based on a proof of concept in which we used Bluetooth Low Energy devices to track the location of each employee in an indoor environment. Among other factors, the location of each individual is assigned a large percentage of the weight to assign a task. This proposal is intended to cover some scenarios of great interest, guaranteeing the correctness of measurement and the privacy of staff tracking. Full article
(This article belongs to the Special Issue Indoor Localization)
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