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CLC number: TP273.5

On-line Access: 2021-01-11

Received: 2020-06-01

Revision Accepted: 2020-07-17

Crosschecked: 2020-09-11

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Kai Da

https://orcid.org/0000-0002-6645-2881

Tiancheng Li

https://orcid.org/0000-0002-0499-5135

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.1 P.5-24

http://doi.org/10.1631/FITEE.2000266


Recent advances in multisensor multitarget tracking using random finite set


Author(s):  Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu

Affiliation(s):  The National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   dktm131@163.com, t.c.li@nwpu.edu.cn, zoyofo@163.com, fanhongqi@nudt.edu.cn

Key Words:  Multitarget tracking, Multisensor fusion, Average fusion, Random finite set, Optimal fusion


Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu. Recent advances in multisensor multitarget tracking using random finite set[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 5-24.

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year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000266"
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A1 - Kai Da
A1 - Tiancheng Li
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DOI - 10.1631/FITEE.2000266


Abstract: 
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.

基于随机有限集的多传感器多目标跟踪研究进展


达凯1,李天成2,朱永锋1,范红旗1,付强1
1国防科技大学ATR国家重点实验室,中国长沙市,410073
2西北工业大学自动化学院信息融合技术教育部重点实验室,中国西安市,710072

摘要:本文综述了基于随机有限集方法的多传感器多目标跟踪的最新研究进展。在多传感器滤波中起基础性作用的融合方法可分为数据层多目标测量融合和评估层多目标密度融合,分别共享融合传感器之间的局部测量值与后验密度。分析每个融合规则的重要属性,包括最优性和次优性。阐述面向不同随机有限集的两种健壮的多目标密度平均方法:算术平均融合与几何平均融合。最后突出强调相关研究主题与现存研究挑战。

关键词:多目标跟踪;多传感器融合;平均融合;随机有限集;最优融合

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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