Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Liu, Feng; Hou, Liming; Wei, Bo; Han, Chongzhao

doi:10.3390/s18124115

Cited by 9 publications

(8 citation statements)

References 48 publications

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“…Moreover, better estimates about the target number and the kinematic states can also be achieved in some challenging scenarios, such as differing densities of measurements, high clutter and proximity among extended targets. We will discuss the extension of the developed approaches for the extended target with complex shape, and extend our current result to the multi-sensor case with appropriate sensor management strategies [37,38] in our future work.…”

Section: Discussionmentioning

confidence: 99%

Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Han

2019

Sensors

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The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

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Section: Discussionmentioning

confidence: 99%

Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Han

2019

Sensors

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“…There have also been fruitful works on sensor control. MTT and sensor control can be unified as a partially observed Markov-decision process, where the control command is usually given by solving an optimization problem (e.g., increasing the observability or reducing the error bound of the system) (Ristic et al, 2013a;Gostar et al, 2017b;Lian et al, 2018aLian et al, , 2018bYan et al, 2020aYan et al, , 2020b.…”

Section: Sensor Registrationmentioning

confidence: 99%

“…Fusion will be executed in each splitting area separately, followed by a reunion operation for all areas. Relevantly, a significant line of research is to constrain the directions and ranges of FoVs through sensor management (Mahler, 2007c;Chen YJ et al, 2017;Lian et al, 2018aLian et al, , 2018b to achieve balance between the surveillance range and the tracking performance.…”

Section: Limited Fields Of Viewmentioning

confidence: 99%

Recent advances in multisensor multitarget tracking using random finite set

Zhu

et al. 2021

Front Inform Technol Electron Eng

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In this study, we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic-and geometric-average fusion, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.

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“…Due to the lack of information of the sensor overlapping and the number of targets in FC, it is necessary to determine the number of targets and duplicate tracks for combining. The action of determining duplicate tracks is known as track-totrack association (TTTA) [8]- [12].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

2019

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The problem of duplicate track determination called ''Track-to-Track association'' occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a density-based fuzzy clustering method for solving the track-to-track association problem in distributed multi-sensor tracking systems. Unlike the previously published solutions, the proposed method does not need any information about the number of targets, due to the use of the density-based clustering approach. Proposed method has low computational overhead and can be used in real-time tracking systems. In addition, the proposed method uses the maximum entropy approach to determine the membership degree of single target related tracks and combines them. This paper presents three scenarios including sensors with complete and incomplete overlapping by considering the bias and a different number of sensors and targets for evaluating the proposed method based on the Monte Carlo simulation. The results indicate the improvement of the efficiency in comparison with the FTF approach. The efficiency of proposed method's results is close to the results of Bayesian minimum mean square error criterion that gives best possible results.INDEX TERMS Density clustering, distributed target tracking, multi sensor fusion, track association.

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Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Cited by 9 publications

References 48 publications

Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Recent advances in multisensor multitarget tracking using random finite set

An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

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