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Multi‐sensors for Indoor Localization and Tracking: 2nd Edition
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Multi‐sensors for Indoor Localization and Tracking: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 2332

Special Issue Editors

Dr. Ran Liu

E-Mail Website
Guest Editor
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: indoor localization; SLAM; robotics; distributed computing; sensor fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Pik Lik Billy Lau

E-Mail Website
Guest Editor
Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: data fusion; multi-robot systems; applied machine learning; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Jianwen Huo

E-Mail Website
Guest Editor
Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang 621010, China
Interests: multi-robot coordination; navigation and localization; sensor fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an increasing interest in indoor positioning due to the rapid growth of location-aware applications, such as city recommendation systems and robot navigation. Although GPS is widely used in outdoor environments, it cannot be applied in indoor scenarios as satellite signals are easily reflected and diffracted by city buildings. A large number of researchers are focusing on localization in GPS-denied environments with various sensors (for example, radio frequency, visual, LiDAR, IMU, and acoustic). Localization in a given infrastructure (i.e., known map or distribution of anchors) has been widely studied. These approaches either require deploying a number of dedicated devices or need a tedious phase to collect radio fingerprints as a representation of a map of the environment. To address this problem, researchers have developed a solution called simultaneous localization and mapping (SLAM), which allows us to simultaneously localize a mobile device and generate a map of the unknown environment.

This Special Issue will address localization and tracking leveraging the power of sensor fusion based on recent techniques. This includes positioning, tracking, indoor mapping, and location-aware applications. 

Topics of interests include, but are not limited to, the following:

  • Simultaneous localization and mapping in robotics;
  • Emerging sensors for indoor localization and tracking;
  • Localization algorithms and implementations;
  • Sensor fusion techniques on localization and tracking;
  • Machine learning for indoor localization;
  • Localization and tracking for the Internet of Things;
  • Location-based services and applications.

Dr. Ran Liu
Dr. Pik Lik Billy Lau
Dr. Jianwen Huo
Guest Editors

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. Sensors 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 2600 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 localization and tracking
  • radio-based localization
  • sensor fusion
  • dead reckoning
  • acoustic sensors
  • Internet of Things
  • smart city
  • SLAM

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Related Special Issue

Published Papers (4 papers)

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Research

15 pages, 10968 KiB  
Article
An Experimental Evaluation of Indoor Localization in Autonomous Mobile Robots
by Mina Khoshrangbaf, Vahid Khalilpour Akram, Moharram Challenger and Orhan Dagdeviren
Sensors 2025, 25(7), 2209; https://doi.org/10.3390/s25072209 - 31 Mar 2025
Viewed by 492
Abstract
High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex [...] Read more.
High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future. Full article
(This article belongs to the Special Issue Multi‐sensors for Indoor Localization and Tracking: 2nd Edition)
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25 pages, 8999 KiB  
Article
Multipath-Assisted Ultra-Wideband Vehicle Localization in Underground Parking Environment Using Ray-Tracing
by Shuo Hu, Lixin Guo, Zhongyu Liu and Shuaishuai Gao
Sensors 2025, 25(7), 2082; https://doi.org/10.3390/s25072082 - 26 Mar 2025
Cited by 1 | Viewed by 314
Abstract
In complex underground parking scenarios, non-line-of-sight (NLOS) obstructions significantly impede positioning signals, presenting substantial challenges for accurate vehicle localization. While traditional positioning approaches primarily focus on mitigating NLOS effects to enhance accuracy, this research adopts an alternative perspective by leveraging NLOS propagation as [...] Read more.
In complex underground parking scenarios, non-line-of-sight (NLOS) obstructions significantly impede positioning signals, presenting substantial challenges for accurate vehicle localization. While traditional positioning approaches primarily focus on mitigating NLOS effects to enhance accuracy, this research adopts an alternative perspective by leveraging NLOS propagation as valuable information, enabling precise positioning in NLOS-dominated environments. We introduce an innovative NLOS positioning framework based on the generalized source (GS) technique, which employs ray-tracing (RT) to transform NLOS paths into equivalent line-of-sight (LOS) paths. A novel GS filtering and weighting strategy to establish initial weights for the nonlinear equation system. To combat significant NLOS noise interference, a robust iterative reweighted least squares (W-IRLS) method synergizes initial weights with optimal position estimation. Integrating ultra-wideband (UWB) delay and angular measurements, four distinct localization modes based on W-IRLS are developed: angle-of-arrival (AOA), time-of-arrival (TOA), AOA/TOA hybrid, and AOA/time-difference-of-arrival (TDOA) hybrid. The comprehensive experimental and simulation results validate the exceptional effectiveness and robustness of the proposed NLOS positioning framework, demonstrating positioning accuracy up to 0.14 m in specific scenarios. This research not only advances the state of the art in NLOS positioning but also establishes a robust foundation for high-precision localization in challenging environments. Full article
(This article belongs to the Special Issue Multi‐sensors for Indoor Localization and Tracking: 2nd Edition)
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24 pages, 4712 KiB  
Article
Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning
by Suqing Yan, Baihui Luo, Xiyan Sun, Jianming Xiao, Yuanfa Ji and Kamarul Hawari bin Ghazali
Sensors 2025, 25(5), 1304; https://doi.org/10.3390/s25051304 - 20 Feb 2025
Viewed by 479
Abstract
Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on [...] Read more.
Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibility. Full article
(This article belongs to the Special Issue Multi‐sensors for Indoor Localization and Tracking: 2nd Edition)
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22 pages, 7321 KiB  
Article
Improving Performance of Bluetooth Low Energy-Based Localization System Using Proximity Sensors and Far-Infrared Thermal Sensor Arrays
by Vitomir Djaja-Josko, Marcin Kolakowski, Jacek Cichocki and Jerzy Kolakowski
Sensors 2025, 25(4), 1151; https://doi.org/10.3390/s25041151 - 13 Feb 2025
Viewed by 627
Abstract
This paper presents the concept of a hybrid positioning scheme using results from a Bluetooth Low Energy (BLE)-based system and additional infrared (IR) devices: proximity sensors and far-infrared thermal sensor arrays. In the proposed solution, the IR sensors operate independently from the BLE [...] Read more.
This paper presents the concept of a hybrid positioning scheme using results from a Bluetooth Low Energy (BLE)-based system and additional infrared (IR) devices: proximity sensors and far-infrared thermal sensor arrays. In the proposed solution, the IR sensors operate independently from the BLE subsystem. Their output (the distance to the localized person and the angle between the sensor axis and the person’s location) is periodically used to improve the positioning accuracy. The results from both parts of the system are fused using a particle-filter-based algorithm. The proposed concept was tested experimentally. The initial tests established that both the proximity (VL53L5CX) and array (MLX90640) sensors allowed for angle estimations with a mean accuracy of about a few degrees. Using them in the proposed hybrid localization scheme resulted in a mean positioning error decrease of several centimeters. Full article
(This article belongs to the Special Issue Multi‐sensors for Indoor Localization and Tracking: 2nd Edition)
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