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Electronics
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New Advances in Navigation and Positioning Systems
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New Advances in Navigation and Positioning Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 5006

Special Issue Editors

Dr. Qu Wang

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: autonomous navigation; multi-source information fusion; GNSS; human activity recognition; inertial navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Ze Li

E-Mail Website
Guest Editor
Key Laboratory of Industrial Internet of Things & Networked Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: indoor localization; wireless sensing
Dr. Meixia Fu

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: intelligent manufacturing; artificial intelligence; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Xia

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beihang University, Beijing100191, China
Interests: pedestrian inertial positioning; wearable sensor-based positioning; motion recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Navigation and location services are basic needs of human social life. They play a basic supporting role in various military and civilian fields and are widely used in emergency rescue, public safety, mass travel, Internet of Things, smart cities, and other fields. To provide seamless and high-precision indoor and outdoor location-based services, researchers have paid increasing attention to indoor positioning. Various indoor positioning and tracking technologies have been applied in academia as well as in industry, ranging from RFID, Zigbee, UWB, Wi-Fi, Bluetooth, visible light, IMU, to magnetic field. However, currently, a general technique widely used, like GNSS, is absent. Each method has its inherent strengths and weaknesses. No single technology dominates the others in terms of accuracy, power consumption, and portability in all practical scenarios.

The aim of the present Special Issue is to foster advances in user activity and location awareness for a wide range of practical applications and research studies. We encourage the submission of both theoretical and applied research results focused on various aspects, including but not limited to, the following:

  • Human activity recognition;
  • Floor recognition;
  • Indoor/outdoor recognition;
  • Autonomous positioning and navigation;
  • Pedestrian positioning;
  • Robot navigation;
  • Magnetic positioning;
  • Radio frequency positioning;
  • Multi-source positioning;
  • Digital twin;
  • Intelligent vehicle;
  • GNSS and applications;
  • Location privacy-preserving.

Dr. Qu Wang
Dr. Ze Li
Dr. Meixia Fu
Dr. Ming Xia
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. Electronics 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 2400 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

  • location-based services
  • multi-source fusion
  • pedestrian dead reckoning
  • simultaneous localization and mapping
  • indoor navigation
  • indoor localization
  • pedestrian navigation
  • human activity recognition
  • vehicle positioning
  • Internet of Things
  • artificial intelligence
  • deep learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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16 pages, 2929 KiB  
Article
TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces
by Yuexia Zhang, Changbao Liu, Yuanshuo Gang and Yu Wang
Electronics 2024, 13(22), 4347; https://doi.org/10.3390/electronics13224347 - 6 Nov 2024
Cited by 3 | Viewed by 1310
Abstract
5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an [...] Read more.
5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy. Full article
(This article belongs to the Special Issue New Advances in Navigation and Positioning Systems)
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28 pages, 8551 KiB  
Article
Enhanced WiFi/Pedestrian Dead Reckoning Indoor Localization Using Artemisinin Optimization-Particle Swarm Optimization-Particle Filter
by Zhihui Liu, Shaojing Song, Jian Chen and Chao Hou
Electronics 2024, 13(17), 3366; https://doi.org/10.3390/electronics13173366 - 24 Aug 2024
Cited by 1 | Viewed by 1555
Abstract
WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, [...] Read more.
WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively. Full article
(This article belongs to the Special Issue New Advances in Navigation and Positioning Systems)
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20 pages, 4622 KiB  
Article
Fingerprint-Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface-Assisted Networks
by Shiru Duan, Yuexia Zhang and Ruiqi Liu
Electronics 2024, 13(9), 1743; https://doi.org/10.3390/electronics13091743 - 1 May 2024
Viewed by 1245
Abstract
The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and [...] Read more.
The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment. Full article
(This article belongs to the Special Issue New Advances in Navigation and Positioning Systems)
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