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Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 7696

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Special Issue Editors

Dr. Santosh Subedi

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Guest Editor

Research and Development Department, Pixii, Kristiansand, Norway
Interests: indoor/outdoor navigation systems; wireless sensor networks; Internet of Things (IoT); machine learning/deep learning; non-intrusive load monitoring (NILM) system; ML-based prescriptive maintenance; LoRaWAN/NB-IoT

Prof. Dr. Jae-Young Pyun

E-Mail Website
Guest Editor

Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: IoT protocols; indoor positioning system (IPS); real-time location system (RTLS); UWB (ultra-wideband)-based IoT applications; IR-UWB; UAV system for public safety
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Special Issue Information

Dear Colleagues,

Due to the rise in smart devices and technologies, social and commercial interests in positioning and navigation systems have been increasing. Similarly, as machine learning (ML) has been widely utilized across various applications, studies on positioning, navigation, and localization systems have significantly adopted ML over the years. Meanwhile, demand for pragmatic positioning services has grown through various applications, such as indoor pathfinding and navigation, marketing, entertainment, security, and location-based information retrieval. Therefore, research and development for indoor and outdoor positioning systems has been expanded.

This Special Issue aims to contribute to developing ML-based positioning algorithms, systems, and applications, utilizing wireless sensors for real-time indoor and outdoor positioning and navigation systems.

Dr. Santosh Subedi
Prof. Dr. Jae-Young Pyun
Guest Editors

Manuscript Submission Information

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Keywords

intelligent platform for indoor spatial data infrastructure
machine learning for positioning and navigation systems
IoT/4G/5G communications for positioning and navigation
WiFi, BLE, and UWB-based positioning and navigation systems
simultaneous localization and mapping (SLAM)
UAV and robotics for positioning and navigation systems
global navigation satellite systems (GNSSs) and satellite-based positioning and navigation systems
indoor/outdoor positioning algorithms, methods, and applications
UWB ranging detection and applications

Published Papers (6 papers)

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Research

33 pages, 5578 KiB

Open AccessArticle

Cramér–Rao Lower Bound for Magnetic Field Localization around Elementary Structures

by Armin Dammann, Benjamin Siebler and Stephan Sand

Sensors 2024, 24(8), 2402; https://doi.org/10.3390/s24082402 - 09 Apr 2024

Viewed by 372

Abstract

The determination of a mobile terminal’s position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth’s magnetic field for positioning. In open areas, the Earth’s magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth’s magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér–Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective. Full article

(This article belongs to the Special Issue Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems)

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25 pages, 1819 KiB

Open AccessArticle

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting

by Hongjie Liu, Feng Liu, Yao Kong and Chaozhong Yang

Sensors 2024, 24(4), 1178; https://doi.org/10.3390/s24041178 - 11 Feb 2024

Viewed by 575

Abstract

Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning. Full article

(This article belongs to the Special Issue Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems)

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29 pages, 18508 KiB

Open AccessArticle

GPS-Free Wireless Precise Positioning System for Automatic Flying and Landing Application of Shipborne Unmanned Aerial Vehicle

by Tsu-Yu Lo, Je-Yao Chang, Tan-Zhi Wei, Pin-Yen Chen, Shih-Ping Huang, Wei-Ting Tsai, Chong-Yi Liou, Chun-Cheng Lin and Shau-Gang Mao

Sensors 2024, 24(2), 550; https://doi.org/10.3390/s24020550 - 15 Jan 2024

Viewed by 655

Abstract

This research is dedicated to developing an automatic landing system for shipborne unmanned aerial vehicles (UAVs) based on wireless precise positioning technology. The application scenario is practical for specific challenging and complex environmental conditions, such as the Global Positioning System (GPS) being disabled during wartime. The primary objective is to establish a precise and real-time dynamic wireless positioning system, ensuring that the UAV can autonomously land on the shipborne platform without relying on GPS. This work addresses several key aspects, including the implementation of an ultra-wideband (UWB) circuit module with a specific antenna design and RF front-end chip to enhance wireless signal reception. These modules play a crucial role in achieving accurate positioning, mitigating the limitations caused by GPS inaccuracy, thereby enhancing the overall performance and reception range of the system. Additionally, the study develops a wireless positioning algorithm to validate the effectiveness of automatic landing on the shipborne platform. The platform’s wave vibration is considered to provide a realistic landing system for shipborne UAVs. The UWB modules are practically installed on the shipborne platform, and the UAV and the autonomous three-body vessel are tested simultaneously in the outdoor open water space to verify the functionality, precision, and adaptability of the proposed UAV landing system. Results demonstrate that the UAV can autonomously fly from 200 m, approach, and automatically land on the moving shipborne platform without relying on GPS. Full article

(This article belongs to the Special Issue Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems)

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15 pages, 1953 KiB

Open AccessArticle

Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach

by Xuxin Lin, Jianwen Gan, Chaohao Jiang, Shuai Xue and Yanyan Liang

Sensors 2023, 23(14), 6320; https://doi.org/10.3390/s23146320 - 12 Jul 2023

Cited by 1 | Viewed by 1497

Abstract

Indoor localization and navigation have become an increasingly important problem in both industry and academia with the widespread use of mobile smart devices and the development of network techniques. The Wi-Fi-based technology shows great potential for applications due to the ubiquitous Wi-Fi infrastructure in public indoor environments. Most existing approaches use trilateration or machine learning methods to predict locations from a set of annotated Wi-Fi observations. However, annotated data are not always readily available. In this paper, we propose a robot-aided data collection strategy to obtain the limited but high-quality labeled data and a large amount of unlabeled data. Furthermore, we design two deep learning models based on a variational autoencoder for the localization and navigation tasks, respectively. To make full use of the collected data, a hybrid learning approach is developed to train the models by combining supervised, unsupervised and semi-supervised learning strategies. Extensive experiments suggest that our approach enables the models to learn effective knowledge from unlabeled data with incremental improvements, and it can achieve promising localization and navigation performance in a complex indoor environment with obstacles. Full article

(This article belongs to the Special Issue Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems)

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19 pages, 5365 KiB

Open AccessArticle

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization

by Chaoyong Yang, Zhenhao Cheng, Xiaoxue Jia, Letian Zhang, Linyang Li and Dongqing Zhao

Sensors 2023, 23(3), 1311; https://doi.org/10.3390/s23031311 - 23 Jan 2023

Cited by 7 | Viewed by 2182

Abstract

For positioning tasks of mobile robots in indoor environments, the emerging positioning technique based on visual inertial odometry (VIO) is heavily influenced by light and suffers from cumulative errors, which cannot meet the requirements of long-term navigation and positioning. In contrast, positioning techniques that rely on indoor signal sources such as 5G and geomagnetism can provide drift-free global positioning results, but their overall positioning accuracy is low. In order to obtain higher precision and more reliable positioning, this paper proposes a fused 5G/geomagnetism/VIO indoor localization method. Firstly, the error back propagation neural network (BPNN) model is used to fuse 5G and geomagnetic signals to obtain more reliable global positioning results; secondly, the conversion relationship from VIO local positioning results to the global coordinate system is established through the least squares principle; and finally, a fused 5G/geomagnetism/VIO localization system based on the error state extended Kalman filter (ES-EKF) is constructed. The experimental results show that the 5G/geomagnetism fusion localization method overcomes the problem of low accuracy of single sensor localization and can provide more accurate global localization results. Additionally, after fusing the local and global positioning results, the average positioning error of the mobile robot in the two scenarios is 0.61 m and 0.72 m. Compared with the VINS-mono algorithm, our approach improves the average positioning accuracy in indoor environments by 69.0% and 67.2%, respectively. Full article

(This article belongs to the Special Issue Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems)

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15 pages, 816 KiB

Open AccessArticle

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

by Fang Cheng, Guofeng Niu, Zhizhong Zhang and Chengjie Hou

Sensors 2022, 22(23), 9531; https://doi.org/10.3390/s22239531 - 06 Dec 2022

Cited by 2 | Viewed by 1513

Abstract

With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method. Full article

(This article belongs to the Special Issue Wireless Sensors and Machine-Learning-Based Algorithms, Systems, and Applications for Practical Positioning and Navigation Systems)

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