Recent advances in multisensor multitarget tracking using random finite set

Kai DA; Tiancheng LI; Yongfeng ZHU; Hongqi FAN; Qiang FU

doi:10.1631/FITEE.2000266

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Recent advances in multisensor multitarget tracking using random finite set

Special Issue on Distributed Filtering and Control of Complex Networks and Systems | Updated：2022-05-19

- Recent advances in multisensor multitarget tracking using random finite set
- 基于随机有限集的多传感器多目标跟踪研究进展
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 1, Pages: 5-24(2021)
- Affiliations：
  
  The National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China
  MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- Author bio：
  
  [ "Kai DA, dktm131@163.com" ]
  Tiancheng LI, E-mail: t.c.li@nwpu.edu.cn
  [ "Yongfeng ZHU, zoyofo@163.com" ]
  [ "Hongqi FAN, fanhongqi@nudt.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.2000266
  CLC： TP273.5
- Received：01 June 2020，
  
  Revised：2020-;9-11，
  
  Published：2021-01
- Accepted：
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Kai DA, Tiancheng LI, Yongfeng ZHU, et al. Recent advances in multisensor multitarget tracking using random finite set[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 5-24.
DOI：

Kai DA, Tiancheng LI, Yongfeng ZHU, et al. Recent advances in multisensor multitarget tracking using random finite set[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 5-24. DOI： 10.1631/FITEE.2000266.

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。

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.

关键词

Keywords

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