Review:Recent advances in multisensor multitarge11racking using random finite set  

作　　者：Kai DA Tiancheng LI Yongfeng ZHU Hongqi FAN Qiang FU 

机构地区：[1]The National Key Laboratory of Science and Technology on ATR,Nalional Universily of Defense Technology,Changsha 410073,China [2]MOE Key Laboratory of Information Fusion Technology,School of Automation,Northwestern Polytechnical University,Xi'an 710072,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2021年第1期5-24,共20页信息与电子工程前沿（英文版）

基　　金：Project supported by the Key Laboratory Foundation of National Defence Technology,China(No.61424010306);the Joint Fund of Equipment Development and Aerospace Science and Technology,China(No.6141B0624050101);the National Natural Science Foundation of China(Nos.61901489 and 62071389)。

摘　　要：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 particulax,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：Multitarget tracking Multisensor fusion Average fusion Random finite set Optimal fusion 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置]