Recent advances in multisensor multitarget tracking using random finite set

2021, Volume 22, Issue 1

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Frontiers of Information Technology & Electronic Engineering >> 2021, Volume 22, Issue 1 doi: 10.1631/FITEE.2000266

Recent advances in multisensor multitarget tracking using random finite set

Affiliation(s): 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, X’an 710072, China; less

Received: 2020-06-01 Accepted: 2021-01-11 Available online: 2021-01-11

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Abstract

In this study, we provide an overview of recent advances in multisensor based on the (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-, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.

Keywords

Multitarget tracking ; Multisensor fusion ; Average fusion ; Random finite set ; Optimal fusion

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