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Multi-Sensor Technology for Tracking, Positioning and Navigation

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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 December 2026 | Viewed by 2753

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Special Issue Editors

Dr. Bingbing Gao

E-Mail Website
Guest Editor

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: multi-sensor technology; navigation and control technology; intelligent sensing and robot positioning technology; information fusion; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Gaoge Hu

E-Mail Website
Guest Editor

School of Automation, Northwestern Polytechnical University, Xi'an, China
Interests: robotic autonomous navigation; inertial-based integrated navigation; deep-space navigation; nonlinear state estimation; Kalman filtering; multi-sensors data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of multi-sensor technology is to combine information from more than one sensor to improve the system accuracy, leading to more specific inferences than using just a single sensor. It has become an indispensable tool for information processing in numerous fields such as target tracking, positioning, navigation, control, attitude estimation, wireless networks, and so on. With the development of information sciences and sensor technology, multi-sensor technology has received greater attention for practical applications in recent years.

This Special Issue aims to bring together original research and review articles on recent advances, solutions, applications, and new challenges for multi-sensor technology in the fields of target tracking, positioning, and navigation.

Potential topics include, but are not limited to, the following:

Multi-sensor technology for target tracking applications;
Multi-sensor technology for positioning and navigation applications;
Artificial intelligence-based multi-sensor technology;
Multi-sensor technology theory;
Multi-sensor-based SLAM;
Multi-sensor-based route planning;
Recent development of multi-sensor technology;
Multi-sensor-based navigation and control;
Multi-sensor technology for wireless network localization.

Dr. Bingbing Gao
Dr. Gaoge Hu
Guest Editors

Manuscript Submission Information

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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

multi-sensor technology
target tracking
positioning and navigation
SLAM
route planning
artificial intelligence

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Multi-Sensor Technology for Target Tracking, Positioning and Navigation in Sensors (20 articles)

Published Papers (5 papers)

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Research

18 pages, 4958 KB

Open AccessArticle

Adaptive Weighted Factor Graph Optimized Positioning Algorithm Based on Joint GNSS/INS/Vision Residual Detection

by Jin Wang, Jun Zou, Yan Xing, Jin Lu, Pengwu Wan and Jianbo Du

Sensors 2026, 26(12), 3783; https://doi.org/10.3390/s26123783 (registering DOI) - 14 Jun 2026

Abstract

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Tracking, Positioning and Navigation)

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36 pages, 3338 KB

Open AccessArticle

A Semantic-Enhanced Multi-Source Fusion Localization Method for GNSS-Degraded Environments

by Haobo Zhao and Xinhua Tang

Sensors 2026, 26(12), 3761; https://doi.org/10.3390/s26123761 (registering DOI) - 12 Jun 2026

Viewed by 150

Abstract

In complex urban environments, Global Navigation Satellite System (GNSS) signals are easily affected by building blockage and multipath effects, which may degrade positioning quality or even cause GNSS denial. As a result, conventional integrated navigation systems suffer from accumulated errors due to insufficient global constraints. To address this problem, a multi-source integrated positioning method incorporating semantic information is proposed. Fixed traffic lights are selected as semantic landmarks, and an object detection network is used to extract the center pixel coordinates and detection confidence of the landmarks. Then, by combining depth information, camera pose, and the prior global coordinates of fixed semantic landmarks, a semantic target inversion model is established to transform two-dimensional image information into three-dimensional position estimates in the world coordinate system. Semantic factors are further constructed and incorporated into backend factor graph optimization. To determine the weighting of semantic factors, the influences of pixel localization error, depth estimation error, camera pose error, and prior coordinate error of fixed semantic landmarks on semantic observations are analyzed, and a noise covariance model for semantic factors is established. Finally, an unmanned ground vehicle experimental platform is built to validate and analyze the proposed factor graph algorithm. The experimental results show that, under GNSS-degraded conditions, the algorithm with semantic factors can provide supplementary global constraints for the system and effectively suppress accumulated positioning errors. In Experiment 1, compared with the algorithm without semantic factors, the maximum absolute trajectory error is reduced by 46.26%. To further verify the applicability of the proposed method in more complex scenarios, Experiment 2 is conducted on a longer route with multiple semantic landmarks and a more severe GNSS-degraded interval. The results show that the proposed method reduces the maximum APE from 6.5432 m to 3.4778 m, corresponding to a reduction of approximately 46.85%. These results demonstrate that the proposed semantic factor can improve the robustness of multi-source fusion localization in GNSS-degraded environments. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Tracking, Positioning and Navigation)

20 pages, 3660 KB

Open AccessArticle

Hybrid Physics-Informed Residual Learning for Robust BDS-3 Satellite Clock Bias Prediction

by Lingfeng Cheng, Keyu Li, Wenhui Guan, Zexian Li, Qin Liang and Chenglin Cai

Sensors 2026, 26(11), 3475; https://doi.org/10.3390/s26113475 - 31 May 2026

Viewed by 287

Abstract

Real-time precise point positioning (RT-PPP) has enabled a wide range of high-precision positioning and navigation applications, while its reliability strongly depends on the availability and continuity of precise satellite clock products. In the third-generation BeiDou Navigation Satellite System (BDS-3), interruptions or gaps in real-time precise clock products can significantly degrade the continuity and performance of precise positioning services. Therefore, accurate and robust satellite clock bias (SCB) prediction is essential for supporting reliable RT-PPP applications under product outage conditions. To address this problem, this study proposes a hybrid physics-informed and data-driven framework for BDS-3 SCB prediction. The proposed method sequentially integrates a physics-informed neural network (PINN) and a long short-term memory (LSTM) network. Specifically, the PINN is used to model and extrapolate the physically consistent trend component of SCB increments by embedding clock dynamical constraints through automatic differentiation, while the LSTM is employed to learn and predict the residual sequence containing short-term stochastic variations that cannot be fully captured by the physical model. The final SCB prediction is obtained by reconstructing the trend and residual components and recovering the original clock bias series. The proposed framework is evaluated using BDS-3 precise clock products and compared with conventional models, including quadratic polynomial (QP), autoregressive integrated moving average (ARIMA), convolutional neural network–long short-term memory (CNN-LSTM), and attention-enhanced long short-term memory (LSTM-Attention). Experimental results show that the proposed PINN-LSTM framework consistently achieves superior prediction accuracy and stability at both 12 h and 24 h forecasting horizons. Specifically, compared with QP, ARIMA, CNN-LSTM, and LSTM-Attention, the proposed method improves prediction accuracy by 18.4%, 52.8%, 32.3%, and 33.8%, respectively, for the 12 h forecasting task, and by 34.8%, 58.5%, 41.8%, and 43.8%, respectively, for the 24 h forecasting task. The results further demonstrate reduced long-horizon error accumulation, improved robustness across satellites equipped with different atomic clock types, and stronger generalization across observation days. These findings indicate that the proposed framework can provide effective support for maintaining the continuity and reliability of BDS-3 precise clock products and has strong potential for improving real-time precise positioning applications. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Tracking, Positioning and Navigation)

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25 pages, 1770 KB

Open AccessArticle

Comparative Evaluation of Bandit-Style Heuristic Policies for Moving Target Detection in a Linear Grid Environment

by Hyunmin Kang, Minho Ahn and Yongduek Seo

Sensors 2026, 26(1), 226; https://doi.org/10.3390/s26010226 - 29 Dec 2025

Viewed by 626

Abstract

Moving-target detection under strict sensing constraints is a recurring subproblem in surveillance, search-and-rescue, and autonomous robotics. We study a canonical one-dimensional finite grid in which a sensor probes one location per time step with binary observations while the target follows reflecting random-walk dynamics. The objective is to minimize the expected time to detection using transparent, training-free decision rules defined on the belief state of the target location. We compare two belief-driven heuristics with purely online implementation: a greedy rule that always probes the most probable location and a belief-proportional sampling (BPS, probability matching) rule that samples sensing locations according to the belief distribution (i.e., posterior probability of the target location). Repeated Monte Carlo simulations quantify the exploitation–exploration trade-off and provide a self-comparison between the two policies. Across tested grid sizes, the greedy policy consistently yields the shortest expected time to detection, improving by roughly 17–20% over BPS and uniform random probing in representative settings. BPS trades some average efficiency for stochastic exploration, which can be beneficial under model mismatch. This study provides an interpretable baseline and quantitative reference for extensions to noisy sensing and higher-dimensional search. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Tracking, Positioning and Navigation)

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22 pages, 1605 KB

Open AccessArticle

High Accuracy Location Tracking for a Hemostasis Stent Achieved by the Fusion of Comprehensively Denoised Magnetic and Inertial Measurements

by Yifan Zhang, William W. Clark, Bryan Tillman, Young Jae Chun, Stephanie Liu and Dahlia Kenawy

Sensors 2025, 25(20), 6498; https://doi.org/10.3390/s25206498 - 21 Oct 2025

Cited by 1 | Viewed by 1058

Abstract

This paper will introduce a location tracking system targeted on a stent when it is deployed into the human artery to achieve hemostasis. This system is proposed to be applied in emergent conditions such as treating injured soldiers on the battlefield where common surgical devices such as fluoroscopy systems are not available. The locating algorithm is based on both magnetic measurements and inertial measurements. The magnetic locating approach detects the sensor’s location in a coordinate system centered with the reference magnet source. The inertial locating approach integrates the linear acceleration and angular velocity measured by the sensor to obtain the angular and linear displacement during a time period. Measurements from all sensors are deeply fused to remove disturbances and noise that degrade the locating accuracy. The focus of this research is to identify all potential error-increasing factors and then provide solutions to correct them to enhance the location measurement reliability. Validation experiments for each improvement approach and the overall locating performance will be introduced. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Tracking, Positioning and Navigation)

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