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31 pages, 10197 KB

Open AccessArticle

A Wi-Fi/PDR Fusion Localization Method Based on Genetic Algorithm Global Optimization

by Linpeng Zhang, Ji Ma, Yanhua Liu, Lian Duan, Yunfei Liang and Yanhe Lu

Sensors 2025, 25(24), 7628; https://doi.org/10.3390/s25247628 - 16 Dec 2025

Viewed by 289

Abstract

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study [...] Read more.

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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21 pages, 4172 KB

Open AccessArticle

OCC-Based Positioning Method for Autonomous UAV Navigation in GNSS-Denied Environments: An Offshore Wind Farm Simulation Study

by Ju-Hyun Kim and Sung-Yoon Jung

Sensors 2025, 25(24), 7569; https://doi.org/10.3390/s25247569 - 12 Dec 2025

Viewed by 359

Abstract

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as [...] Read more.

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as optical transmitters and a UAV-mounted camera as the receiver. In the proposed system, absolute positional identifiers are encoded into color-shift-keying-modulated optical signals emitted by fixed LEDs and captured by the UAV camera. The UAV’s 3D position is estimated by integrating the decoded LED information with geometric constraints through the Perspective-n-Point algorithm, eliminating the need for satellite or RF-based localization infrastructure. A virtual offshore wind farm, developed in Unreal Engine, was used to experimentally evaluate the feasibility and accuracy of the method. Results demonstrate submeter localization precision over a 50,000 cm flight path, confirming the system’s capability for reliable, real-time positioning. These findings indicate that OCC-based positioning provides a cost-effective and robust alternative for UAV navigation in complex or communication-restricted environments. The offshore wind farm inspection scenario further highlights the method’s potential for industrial operation and maintenance tasks and underscores the promise of integrating optical wireless communication into autonomous UAV systems. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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30 pages, 12213 KB

Open AccessArticle

A Two-Stage Framework for Sensor Selection and Geolocation for eVTOL Emergency Localization Using HF Skywaves

by Xijun Liu, Houlong Ai, Chen Xu, Zelin Chen and Zhaoyang Li

Sensors 2025, 25(24), 7534; https://doi.org/10.3390/s25247534 - 11 Dec 2025

Viewed by 472

Abstract

High-Frequency (HF) geolocation is crucial for emergency search and rescue operations and for re-geolocation of missing targets. This paper proposes a two-stage (Receiver selection then geolocation with Random Spatial Spectrum (RSS)) framework with HF skywave propagation as the main link, which is suitable [...] Read more.

High-Frequency (HF) geolocation is crucial for emergency search and rescue operations and for re-geolocation of missing targets. This paper proposes a two-stage (Receiver selection then geolocation with Random Spatial Spectrum (RSS)) framework with HF skywave propagation as the main link, which is suitable for scenarios where the electric Vertical Take-off and Landing (eVTOL) aircraft loses contact, crashes, or has communication interruption after a malfunction. First, stage A employs two receiver selection paths. One is selection with unknown biases, which combines geometric observability to determine receiver selection. The other is selection with bias priors, which introduces non-line-of-sight bias priors and robust weighting to improve availability. Secondly, stage B constructs RSS-based geolocation using grid objective function matching to alleviate the sensitivity of explicit time difference estimation to noise and model mismatch, thereby maintaining robustness under non-line-of-sight (NLOS) conditions. Finally, simulation and actual measurements demonstrate that the “select first, geolocation later” approach achieves superior overall performance compared to direct geolocation without receiver selection. This study provides a methodological basis and initial field evidence for HF skywave-based emergency eVTOL geolocation. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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19 pages, 3009 KB

Open AccessArticle

Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System

by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek and Patricia Nieva

Sensors 2025, 25(9), 2638; https://doi.org/10.3390/s25092638 - 22 Apr 2025

Cited by 1 | Viewed by 2658

Abstract

Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that [...] Read more.

Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that focus on reduced power consumption and cost. Bluetooth low energy (BLE) technology is energy- and cost-efficient compared to other technologies. Integrating BLE Received Signal Strength Indicator (RSSI) signals with machine learning (ML) introduces a new Artificial Intelligence- (AI-) enhanced OM approach. In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). This approach differs from other technologies in that it does not require any floorplan information. The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual’s location is classified, introducing a highly reliable indoor/outdoor tracking system. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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20 pages, 4186 KB

Open AccessArticle

Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics

by Arturs Simkuns, Rodions Saltanovs, Maksims Ivanovs and Roberts Kadikis

Sensors 2025, 25(5), 1576; https://doi.org/10.3390/s25051576 - 4 Mar 2025

Cited by 1 | Viewed by 2942

Abstract

Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable [...] Read more.

Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network’s units exhibited spatially periodic and hexagonal activation patterns characteristic of biological grid cells, as well as responses resembling border cells and head-direction cells. These findings demonstrate that grid cell networks can effectively learn spatial representations from robot trajectories, providing a foundation for developing advanced navigation algorithms for mobile robots. We conclude by discussing current challenges and future research directions in this field. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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22 pages, 1654 KB

Open AccessEditor’s ChoiceArticle

A New Scene Sensing Model Based on Multi-Source Data from Smartphones

by Zhenke Ding, Zhongliang Deng, Enwen Hu, Bingxun Liu, Zhichao Zhang and Mingyang Ma

Sensors 2024, 24(20), 6669; https://doi.org/10.3390/s24206669 - 16 Oct 2024

Cited by 1 | Viewed by 1741

Abstract

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect [...] Read more.

Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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13 pages, 4239 KB

Open AccessCommunication

Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks

by Runjie Liu, Qionggui Zhang, Yuankang Zhang, Rui Zhang and Tao Meng

Sensors 2024, 24(16), 5335; https://doi.org/10.3390/s24165335 - 18 Aug 2024

Cited by 5 | Viewed by 2382

Abstract

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across [...] Read more.

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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Review

Jump to: Research

21 pages, 1744 KB

Open AccessReview

A Review of Miniature Radio Transmitters for Wildlife Tracking

by Sivan Toledo

Sensors 2025, 25(2), 517; https://doi.org/10.3390/s25020517 - 17 Jan 2025

Cited by 2 | Viewed by 5405

Abstract

This article surveys the literature on miniature radio transmitters designed to track free-ranging wild animals using emitter-localization techniques. The articles covers the topics of power sources used in such transmitters, including miniature batteries and energy harvesting, techniques for generating the transmitted radio-frequency carrier, [...] Read more.

This article surveys the literature on miniature radio transmitters designed to track free-ranging wild animals using emitter-localization techniques. The articles covers the topics of power sources used in such transmitters, including miniature batteries and energy harvesting, techniques for generating the transmitted radio-frequency carrier, techniques for creating short radio pulses and more general on–off schedules, modulation in modern wildlife-tracking transmitters, construction, manufacturing, and tuning techniques, and recent trends in this area. The article also describes the recreation of the first successful wildlife-tracking transmitter, a nontrivial invention that had a profound impact on wildlife ecology, and explores its behavior. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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