Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking

Abstract The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi‐target tracking (MTT) with unknown,time‐varying number of targets. However, these methods can only be used in MTT systems with some prior...

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Autores principales:	Jinlong Yang, Jiuliu Tao, Yuan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/b40b453497ba44279a188ba993925912
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spelling	oai:doaj.org-article:b40b453497ba44279a188ba9939259122021-11-09T10:16:47ZAdaptive grid‐driven probability hypothesis density filter for multi‐target tracking1751-96831751-967510.1049/sil2.12063https://doaj.org/article/b40b453497ba44279a188ba9939259122021-12-01T00:00:00Zhttps://doi.org/10.1049/sil2.12063https://doaj.org/toc/1751-9675https://doaj.org/toc/1751-9683Abstract The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi‐target tracking (MTT) with unknown,time‐varying number of targets. However, these methods can only be used in MTT systems with some prior information of multipletargets, such asdynamic model, newborn target distribution etc.;otherwise, the tracking performance will decline greatly. To solve this problem,an adaptive Grid‐driven technique is proposed based on the framework of the PHD/CPHD filter to recursively estimate the target states without knowing the dynamic model and the newborn target distribution. The grid size can be adaptively adjusted according to the grid resolution, and the dynamic tendencies of the grids can respond to the unknown dynamic models of each targets, including arbitrary manoeuvring models. The newborn targets outside the grid area can be identified by analysing the measurements, and some new grids are generated around them. The experimental results show that the proposed algorithm has a better performance than the traditional particle filter‐based PHD method in terms of average optimal sub‐pattern assignment distance and average target number estimation for tracking multiple targets with unknown dynamic parameters and unknown newborn target distribution.Jinlong YangJiuliu TaoYuan ZhangWileyarticleTelecommunicationTK5101-6720ENIET Signal Processing, Vol 15, Iss 9, Pp 584-596 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Jinlong Yang Jiuliu Tao Yuan Zhang Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
description	Abstract The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi‐target tracking (MTT) with unknown,time‐varying number of targets. However, these methods can only be used in MTT systems with some prior information of multipletargets, such asdynamic model, newborn target distribution etc.;otherwise, the tracking performance will decline greatly. To solve this problem,an adaptive Grid‐driven technique is proposed based on the framework of the PHD/CPHD filter to recursively estimate the target states without knowing the dynamic model and the newborn target distribution. The grid size can be adaptively adjusted according to the grid resolution, and the dynamic tendencies of the grids can respond to the unknown dynamic models of each targets, including arbitrary manoeuvring models. The newborn targets outside the grid area can be identified by analysing the measurements, and some new grids are generated around them. The experimental results show that the proposed algorithm has a better performance than the traditional particle filter‐based PHD method in terms of average optimal sub‐pattern assignment distance and average target number estimation for tracking multiple targets with unknown dynamic parameters and unknown newborn target distribution.
format	article
author	Jinlong Yang Jiuliu Tao Yuan Zhang
author_facet	Jinlong Yang Jiuliu Tao Yuan Zhang
author_sort	Jinlong Yang
title	Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
title_short	Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
title_full	Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
title_fullStr	Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
title_full_unstemmed	Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
title_sort	adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b40b453497ba44279a188ba993925912
work_keys_str_mv	AT jinlongyang adaptivegriddrivenprobabilityhypothesisdensityfilterformultitargettracking AT jiuliutao adaptivegriddrivenprobabilityhypothesisdensityfilterformultitargettracking AT yuanzhang adaptivegriddrivenprobabilityhypothesisdensityfilterformultitargettracking
_version_	1718441139895271424

Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking

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