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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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12063 |
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| author | Jinlong Yang Jiuliu Tao Yuan Zhang |
| author_facet | Jinlong Yang Jiuliu Tao Yuan Zhang |
| author_sort | Jinlong Yang |
| collection | DOAJ |
| 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 |
| id | doaj-art-b40b453497ba44279a188ba993925912 |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-b40b453497ba44279a188ba9939259122025-02-03T06:47:26ZengWileyIET Signal Processing1751-96751751-96832021-12-0115958459610.1049/sil2.12063Adaptive grid‐driven probability hypothesis density filter for multi‐target trackingJinlong Yang0Jiuliu Tao1Yuan Zhang2School of Artificial Intelligence and Computer Science Jiangnan University Wuxi ChinaSchool of Artificial Intelligence and Computer Science Jiangnan University Wuxi ChinaSchool of Artificial Intelligence and Computer Science Jiangnan University Wuxi ChinaAbstract 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.https://doi.org/10.1049/sil2.12063filtering theoryparticle filtering (numerical methods)probabilityrecursive estimationtarget tracking |
| spellingShingle | Jinlong Yang Jiuliu Tao Yuan Zhang Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking IET Signal Processing filtering theory particle filtering (numerical methods) probability recursive estimation target tracking |
| title | 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_short | Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking |
| title_sort | adaptive grid driven probability hypothesis density filter for multi target tracking |
| topic | filtering theory particle filtering (numerical methods) probability recursive estimation target tracking |
| url | https://doi.org/10.1049/sil2.12063 |
| work_keys_str_mv | AT jinlongyang adaptivegriddrivenprobabilityhypothesisdensityfilterformultitargettracking AT jiuliutao adaptivegriddrivenprobabilityhypothesisdensityfilterformultitargettracking AT yuanzhang adaptivegriddrivenprobabilityhypothesisdensityfilterformultitargettracking |