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Target Detection, Tracking and Identification Using Multi-Sensor Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 11022

Special Issue Editors


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Guest Editor
School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China
Interests: multi-target tracking; sensor networks; resources management; multi-sensor information fusion
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of Electronic Science Technology of China, Chengdu 610054, China
Interests: wireless positioning; machine learning; MQS; transfer learning; multi-agent interactive
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China
Interests: multi-target tracking; sensor networks; resources management; multi-sensor information fusion

Special Issue Information

Dear Colleagues,

Due to the physical limitations of sensing ability, target detection, tracking and identification (DTI) with a single sensor can no longer meet the increasing requirements of both civilian and military applications. Thanks to the rapid development of wireless communication and distributed computing technologies, cooperative sensing using multi-sensor systems provides a natural and effective solution to address these challenges. Integrating information from multiple sensors, possibly of different types and backgrounds, can yield performance superior to that of any single sensor. As a result, multi-sensor cooperative detection, tracking, and identification are gaining considerable attention in autonomous vehicles, distributed multi-static radar systems and various large-scale systems.

The core topics of this research include distributed filtering, sensor registration and control, network consensus and synchronization, multi-source data clustering/fusion and network topological design/analysis. These problems are imperative but also challenging in terms of their multidisciplinary nature and inherent complexity. Various theories, techniques and algorithms, including some learning-based methods, are continuously being proposed and developed, and yet it more advanced research efforts and endeavors are still called for.

This Special Issue will focus on the latest advances in multi-sensor cooperative detection, tracking and identification. Prospective authors are invited to submit novel and original manuscripts about its technological underpinnings and practical applications, as well as providing an overview of the state-of-the-art techniques and future applications. Potential topics of interest include, but are not limited to:

  • Efficient multi-sensor signal and data processing methods.
  • Cognitive sensing and learning methods for multi-sensor systems.
  • Sensor management and resource aware design for target detection, localization and tracking using multi-sensor systems.
  • Multi-sensor data clustering, flooding, fitting and learning.
  • Artificial intelligence approaches to multi-sensor target detection, tracking and identification.
  • Distributed MIMO and multi-static radar systems.
  • Joint sensor registration/control and target tracking in multi-sensor systems.
  • Out-of-sequence measurements and tracks in multi-sensor multi-target systems.
  • Measures of performance for multi-sensor multi-target systems.

Prof. Dr. Wei Yi
Prof. Dr. Xiansheng Guo
Dr. Ye Yuan
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.

Published Papers (7 papers)

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Research

16 pages, 729 KiB  
Article
Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network
by Zhan Chen, Yangwang Fang, Ruitao Zhang and Wenxing Fu
Sensors 2024, 24(8), 2415; https://doi.org/10.3390/s24082415 - 10 Apr 2024
Viewed by 436
Abstract
The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a [...] Read more.
The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a bearing-only sensor network to perform layout planning on the geometric configuration of the optimal detection. Firstly, the system model of a bearing-only sensor network was constructed, and the observability of the system was analyzed based on the Cramer–Rao Lower Bound and Fisher Information Matrix. Then, in view of the limitations of the traditional mayfly algorithm, which has a single initial population and no adaptability and poor global search capabilities, multi-strategy fusion improvements were carried out by introducing Tent chaos mapping, the adaptive inertia weight factor, and Random Opposition-based Learning. Finally, three simulation experiments were conducted. Through comparison with the Particle Swarm Optimization (PSO) algorithm, Mayfly Algorithm (MA), and Genetic Algorithm (GA), the effectiveness and superiority of the proposed MIAMA were validated. Full article
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24 pages, 2679 KiB  
Article
Sensor Management Method of Giving Priority to Confirmed Identified Targets
by Chunshan Ding and Chunguo Li
Sensors 2023, 23(8), 3959; https://doi.org/10.3390/s23083959 - 13 Apr 2023
Viewed by 984
Abstract
The optimization objective function of sensor management for target identification is commonly established based on information theory indicators such as information gain, discrimination, discrimination gain, and quadratic entropy, which can control the sensors to reduce the overall uncertainty of all targets to be [...] Read more.
The optimization objective function of sensor management for target identification is commonly established based on information theory indicators such as information gain, discrimination, discrimination gain, and quadratic entropy, which can control the sensors to reduce the overall uncertainty of all targets to be identified but ignores the speed of target being confirmed as identified. Therefore, inspired by the maximum posterior criterion of target identification and the target identification confirmation mechanism, we study a sensor management method that preferentially allocates resources to identifiable targets. Firstly, in the distributed target identification framework based on Bayesian theory, an improved identification probability prediction method that provides feedback the global identification results to local classifiers is proposed, which can improve the accuracy of identification probability prediction. Secondly, an effective sensor management function based on information entropy and expected confidence level is proposed to optimize the identification uncertainty itself rather than its variation, which can increase the priority of targets that satisfy the desired confidence level. In the end, the sensor management for target identification is modeled as a sensor allocation problem, and the optimization objective function based on the effective function is constructed, which can improve the target identification speed. The experimental results show that the correct identification rate of the proposed method is comparable to the methods based on information gain, discrimination, discrimination gain, and quadratic entropy in different scenarios, but the average time to confirm the identification is the shortest. Full article
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17 pages, 2806 KiB  
Article
CSMOT: Make One-Shot Multi-Object Tracking in Crowded Scenes Great Again
by Haoxiong Hou, Chao Shen, Ximing Zhang and Wei Gao
Sensors 2023, 23(7), 3782; https://doi.org/10.3390/s23073782 - 6 Apr 2023
Viewed by 2177
Abstract
The current popular one-shot multi-object tracking (MOT) algorithms are dominated by the joint detection and embedding paradigm, which have high inference speeds and accuracy, but their tracking performance is unstable in crowded scenes. Not only does the detection branch have difficulty in obtaining [...] Read more.
The current popular one-shot multi-object tracking (MOT) algorithms are dominated by the joint detection and embedding paradigm, which have high inference speeds and accuracy, but their tracking performance is unstable in crowded scenes. Not only does the detection branch have difficulty in obtaining the accurate object position, but the ambiguous appearance of features extracted by the re-identification (re-ID) branch also leads to identity switches. Focusing on the above problems, this paper proposes a more robust MOT algorithm, named CSMOT, based on FairMOT. First, on the basis of the encoder–decoder network, a coordinate attention module is designed to enhance the information interaction between channels (horizontal and vertical coordinates), which improves its object-detection abilities. Then, an angle-center loss that effectively maximizes intra-class similarity is proposed to optimize the re-ID branch, and the extracted re-ID features are made more discriminative. We further redesign the re-ID feature dimension to balance the detection and re-ID tasks. Finally, a simple and effective data association mechanism is introduced, which associates each detection instead of just the high-score detections during the tracking process. The experimental results show that our one-shot MOT algorithm achieves excellent tracking performance on multiple public datasets and can be effectively applied to crowded scenes. In particular, CSMOT decreases the number of ID switches by 11.8% and 33.8% on the MOT16 and MOT17 test datasets, respectively, compared to the baseline. Full article
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30 pages, 8215 KiB  
Article
An LEO Constellation Early Warning System Decision-Making Method Based on Hierarchical Reinforcement Learning
by Yu Cheng, Cheng Wei, Shengxin Sun, Bindi You and Yang Zhao
Sensors 2023, 23(4), 2225; https://doi.org/10.3390/s23042225 - 16 Feb 2023
Cited by 1 | Viewed by 1692
Abstract
The cooperative positioning problem of hypersonic vehicles regarding LEO constellations is the focus of this research study on space-based early warning systems. A hypersonic vehicle is highly maneuverable, and its trajectory is uncertain. New challenges are posed for the cooperative positioning capability of [...] Read more.
The cooperative positioning problem of hypersonic vehicles regarding LEO constellations is the focus of this research study on space-based early warning systems. A hypersonic vehicle is highly maneuverable, and its trajectory is uncertain. New challenges are posed for the cooperative positioning capability of the constellation. In recent years, breakthroughs in artificial intelligence technology have provided new avenues for collaborative multi-satellite intelligent autonomous decision-making technology. This paper addresses the problem of multi-satellite cooperative geometric positioning for hypersonic glide vehicles (HGVs) by the LEO-constellation-tracking system. To exploit the inherent advantages of hierarchical reinforcement learning in intelligent decision making while satisfying the constraints of cooperative observations, an autonomous intelligent decision-making algorithm for satellites that incorporates a hierarchical proximal policy optimization with random hill climbing (MAPPO-RHC) is designed. On the one hand, hierarchical decision making is used to reduce the solution space; on the other hand, it is used to maximize the global reward and to uniformly distribute satellite resources. The single-satellite local search method improves the capability of the decision-making algorithm to search the solution space based on the decision-making results of the hierarchical proximal policy-optimization algorithm, combining both random hill climbing and heuristic methods. Finally, the MAPPO-RHC algorithm’s coverage and positioning accuracy performance is simulated and analyzed in two different scenarios and compared with four intelligent satellite decision-making algorithms that have been studied in recent years. From the simulation results, the decision-making results of the MAPPO-RHC algorithm can obtain more balanced resource allocations and higher geometric positioning accuracy. Thus, it is concluded that the MAPPO-RHC algorithm provides a feasible solution for the real-time decision-making problem of the LEO constellation early warning system. Full article
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18 pages, 898 KiB  
Article
A Combined mmWave Tracking and Classification Framework Using a Camera for Labeling and Supervised Learning
by Andre Pearce, J. Andrew Zhang and Richard Xu
Sensors 2022, 22(22), 8859; https://doi.org/10.3390/s22228859 - 16 Nov 2022
Cited by 2 | Viewed by 1319
Abstract
Millimeter wave (mmWave) radar poses prosperous opportunities surrounding multiple-object tracking and sensing as a unified system. One of the most challenging aspects of exploiting sensing opportunities with mmWave radar is the labeling of mmWave data so that, in turn, a respective model can [...] Read more.
Millimeter wave (mmWave) radar poses prosperous opportunities surrounding multiple-object tracking and sensing as a unified system. One of the most challenging aspects of exploiting sensing opportunities with mmWave radar is the labeling of mmWave data so that, in turn, a respective model can be designed to achieve the desired tracking and sensing goals. The labeling of mmWave datasets usually involves a domain expert manually associating radar frames with key events of interest. This is a laborious means of labeling mmWave data. This paper presents a framework for training a mmWave radar with a camera as a means of labeling the data and supervising the radar model. The methodology presented in this paper is compared and assessed against existing frameworks that aim to achieve a similar goal. The practicality of the proposed framework is demonstrated through experimentation in varying environmental conditions. The proposed framework is applied to design a mmWave multi-object tracking system that is additionally capable of classifying individual human motion patterns, such as running, walking, and falling. The experimental findings demonstrate a reliably trained radar model that uses a camera for labeling and supervision that can consistently produce high classification accuracy across environments beyond those in which the model was trained against. The research presented in this paper provides a foundation for future research in unified tracking and sensing systems by alleviating the labeling and training challenges associated with designing a mmWave classification model. Full article
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21 pages, 3253 KiB  
Article
Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks
by Yang Chen, Yideng Cao and Rui Wang
Sensors 2022, 22(19), 7160; https://doi.org/10.3390/s22197160 - 21 Sep 2022
Cited by 3 | Viewed by 1329
Abstract
A probabilistic data association-based distributed cubature Kalman filter (PDA-DCKF) method is proposed in this paper, whose performance on tracking single moving sound sources in the distributed acoustic sensor network was verified. In this method, the PDA algorithm is first used to sift the [...] Read more.
A probabilistic data association-based distributed cubature Kalman filter (PDA-DCKF) method is proposed in this paper, whose performance on tracking single moving sound sources in the distributed acoustic sensor network was verified. In this method, the PDA algorithm is first used to sift the observations from neighboring nodes. Then, the sifted observations are fused to update the state vectors in the CKF. Since nodes in a sensor network have different reliabilities, the final tracking result integrates the estimations from the local nodes, which are weighted with the parameters depending on the mean square error of the estimation and the energy of the received signal. The experimental results illustrated that the proposed PDA-DCKF method is superior to the other DCKF methods in tracking sound sources even under severe noise and reverberant conditions. Full article
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14 pages, 746 KiB  
Article
Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification
by Dong-Hyuk Yang and Yong-Shin Kang
Sensors 2022, 22(14), 5095; https://doi.org/10.3390/s22145095 - 7 Jul 2022
Cited by 2 | Viewed by 1575
Abstract
Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; [...] Read more.
Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; however, it only focuses on the mean value of each segment in the time-series. Here, we propose a novel time-series representation method—distance- and momentum-based symbolic aggregate approximation (DM-SAX)—that can secure time-series distributions by calculating the perpendicular distance from the time-axis to each data point and consider the time-series trend by adding a momentum factor reflecting the direction of previous data points. Experimental results for 29 highly imbalanced classification problems on the UCR datasets revealed that DM-SAX affords the optimal area under the curve (AUC) among competing time-series representation methods (SAX, extreme-SAX, overlap-SAX, and distance-based SAX). We statistically verified that performance improvements resulted in significant differences in the rankings. In addition, DM-SAX yielded the optimal AUC for real-world wire cutting and crimping process dataset. Meaningful data points such as outliers could be identified in a time-series outlier detection framework via the proposed method. Full article
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