Next Generation Indoor Positioning Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 5713

Special Issue Editors

Geospatial Technologies Research Group (GEOTEC), Universitat Jaume I of Castellón, Castellón, Spain
Interests: geographic information systems and technologies; GI applications; mobile and wearable computing; health informatics
Special Issues, Collections and Topics in MDPI journals
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere‎, Finland
Interests: wireless communications; information security; authentication; distributed systems; blockchain; resource-constrained devices; wearable technology
Special Issues, Collections and Topics in MDPI journals
Institute of New Imaging Technologies Espaitec 2, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, Castelló de la Plana, Spain
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localisation and positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The positioning of humans, robots, and electronic devices is quintessential for a myriad of modern applications. For outdoor positioning, applications often rely on global navigation satellite systems (GNSSs) such as GPS, Galileo, BeiDou, or GLONASS, possibly combined with GNSS correction algorithms and services—even though the new generation of limitedly powered micro-devices challenges the status quo. Positioning in indoor environments, with varying geometric properties, multiple sources of signal interference, and a broad range of application scenarios and associated user requirements, is more complex. For over two decades, researchers have been exploring various technologies (e.g., WiFi, inertial measurement units, ultra-wideband, Bluetooth, 5G), techniques (e.g., received signal strength indicator, dead reckoning, fingerprinting, time of arrival/flight), and methods (e.g., pedestrian dead reckoning, ranging, multilateration, k-NN, various RSSI- and fingerprinting-based methods), without a clear winning combination, even for similar scenarios/applications. Further research is needed on both novel and existing technologies, techniques, and methods; their strengths and weaknesses in various environments, circumstances, and usage scenarios; and the implications of their use with respect to aspects such as power consumption, performance, efficiency, robustness, privacy, security, maintenance, and related issues.

This Special Issue solicits original research articles contributing to the state-of-the-art in indoor positioning systems, including novel conceptual and theoretical solutions, the validation and evaluation of new or existing systems, comparative studies, data sets, and practical applications of indoor positioning systems. Authors are encouraged to apply reproducibility practices, making the datasets, algorithms and programming code publicly available.

Topics of interest include but are not limited to:

  • Artificial intelligence for indoor positioning systems;
  • Centralized and decentralized indoor positioning systems;
  • Collaborative indoor positioning systems;
  • Comparative studies for indoor positioning systems;
  • Computational approximations for positioning systems;
  • Contact tracing applications;
  • Data fusion for indoor positioning;
  • Data privacy and security in indoor positioning systems;
  • Positioning hardware and receiver design;
  • Hybrid indoor positioning systems;
  • Indoor positioning datasets, use cases and applications;
  • Indoor positioning validations and evaluations;
  • Infrastructure and infrastructureless indoor positioning systems;
  • Integrated indoor/outdoor positioning systems;
  • Maintenance, lifetime and performance degradation over time;
  • New technologies for indoor positioning systems (e.g., 5G, 6G);
  • Novel indoor positioning technologies, techniques or methods;
  • Robustness, deployability and cost of indoor positioning systems.

Dr. Sven Casteleyn
Dr. Aleksandr Ometov
Dr. Joaquín Torres-Sospedra
Guest Editors

Manuscript Submission Information

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Keywords

  • indoor positioning systems
  • indoor localization systems
  • artificial intelligence
  • wireless communications

Published Papers (4 papers)

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Research

20 pages, 5844 KiB  
Article
Multi-Output Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization
Appl. Sci. 2023, 13(22), 12167; https://doi.org/10.3390/app132212167 - 09 Nov 2023
Viewed by 545
Abstract
In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with [...] Read more.
In the evolving landscape of device-free localization techniques, Wi-Fi channel state information (CSI) emerges as a pivotal tool for environmental sensing. This study introduces a novel fingerprint localization algorithm. It employs an improved Hybrid Grey Wolf Particle Swarm Optimization (IPSOGWO) in combination with Multi-Output Support Vector Regression (MSVR) to enhance indoor positioning accuracy. To counteract the limitations of standard DBSCAN and PCA in noise reduction and feature extraction from complex nonlinear data, we propose an adaptive denoising algorithm based on spatial clustering (A-DBSCAN) and an autoencoder to efficiently denoise and extract features from CSI amplitude to improve the localization accuracy. Additionally, we introduce a new position update strategy, bolstering the optimization efficiency of the PSOGWO algorithm. This refined approach is instrumental in determining the globally optimal hyperparameters in MSVR, leading to enhanced model prediction accuracy. Two indoor scenario experiments were conducted to evaluate our method, yielding average localization errors of 0.59 m and 1.12 m, marking an improvement in localization performance compared to existing methods. Full article
(This article belongs to the Special Issue Next Generation Indoor Positioning Systems)
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17 pages, 3073 KiB  
Article
Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation
Appl. Sci. 2023, 13(18), 10046; https://doi.org/10.3390/app131810046 - 06 Sep 2023
Cited by 1 | Viewed by 791
Abstract
With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer [...] Read more.
With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model). Full article
(This article belongs to the Special Issue Next Generation Indoor Positioning Systems)
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19 pages, 5360 KiB  
Article
Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
Appl. Sci. 2023, 13(10), 6187; https://doi.org/10.3390/app13106187 - 18 May 2023
Cited by 1 | Viewed by 927
Abstract
Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB’s positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) [...] Read more.
Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB’s positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) and extended Kalman filter (EKF) would also visibly degrade UWB positioning accuracy. To overcome the impacts of NLOS errors and truncation errors, this paper introduced a robust-theory-based particle filter (RPF) into UWB positioning. In such a method, the IGG-III model and PF were adopted to limit the impacts of NLOS errors and truncation errors, respectively, by introducing a weight inflation factor and particle group. For comparison, the Bancroft, LS, EKF, unscented Kalman filter (UKF), cubature Kalman filter (CKF), PF, and RPF were also presented. Here, the influences of truncation errors were analyzed by comparing the results based on LS and EKF with those calculated by UKF, CKF, and PF. The impacts of NLOS errors were evaluated by making a comparison between the results of PF and RPF. Results based on a set of simulated UWB data and a group of experiment UWB data demonstrated that the RPF can significantly avoid the positioning errors caused by both truncation errors and NLOS errors. In general, position improvements percentages of 57.2%, 52.7%, 39.6%, 38.2%, 26.6%, and 20.4% can be obtained by RPF compared to those calculated by Bancroft, LS, EKF, UKF, CKF, and PF, respectively. As a comparison, the truncation error would lead to about 8.1%, 10.1%, and 33.2% accuracy decrease in the north, east, and vertical directions on average. Such accuracy-decrease rates caused by NLOS were 6.1%, 5.2%, and 25%. Full article
(This article belongs to the Special Issue Next Generation Indoor Positioning Systems)
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17 pages, 6648 KiB  
Article
Outlier Detection in Time-Series Receive Signal Strength Observation Using Z-Score Method with Sn Scale Estimator for Indoor Localization
Appl. Sci. 2023, 13(6), 3900; https://doi.org/10.3390/app13063900 - 19 Mar 2023
Cited by 10 | Viewed by 2755
Abstract
Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation [...] Read more.
Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation (MAD) scale estimator has been used to detect outliers, but it is only efficient with symmetrically distributed observations. Experimental analysis has shown that time-series RSS observations can have a symmetric or asymmetric distribution depending on the nature of the environment in which the measurement was taken. Hence, the use of the Z-score method with the MAD scale estimator will not be efficient. In this paper, the Sn scale estimator is proposed as an alternative to MAD to be used with the Z-score method in detecting outliers in time-series RSS observations. Performance comparison using an online RSS dataset shows that the Z-score with MAD and Sn as scale estimators falsely detected about 50% and 13%, respectively, of the RSS observations as outliers. Furthermore, the average absolute RSS median deviations between raw and outlier-free observations are 3 dB and 0.25 dB, respectively, for the MAD and Sn scale estimators, corresponding to a range error of about 2 m and 0.5 m. Full article
(This article belongs to the Special Issue Next Generation Indoor Positioning Systems)
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