Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking
Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increas...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5339 |
| _version_ | 1797443830985785344 |
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| author | Jin Tao Defu Jiang Jialin Yang Chao Zhang Song Wang Yan Han |
| author_facet | Jin Tao Defu Jiang Jialin Yang Chao Zhang Song Wang Yan Han |
| author_sort | Jin Tao |
| collection | DOAJ |
| description | Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increases exponentially. The Gaussian mixture probability hypothesis density (GM-PHD) filtering based on a random finite set (RFS) provides an effective method to solve multi-target tracking problems without the requirement of explicit data association. However, it is difficult to track targets accurately in real-time with dense clutter and low detection probability. To solve this problem, this paper proposes a multi-feature matching GM-PHD (MFGM-PHD) filter for radar multi-target tracking. Using Doppler and amplitude information contained in radar echo to modify the weights of Gaussian components, the weight of the clutter can be greatly reduced and the target can be distinguished from clutter. Simulations show that the proposed MFGM-PHD filter can improve the accuracy of multi-target tracking as well as the real-time performance with high clutter density and low detection probability. |
| first_indexed | 2024-03-09T13:02:43Z |
| format | Article |
| id | doaj.art-60bf2f9c571c42daa77938da69c00ad7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:02:43Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-60bf2f9c571c42daa77938da69c00ad72023-11-30T21:52:12ZengMDPI AGSensors1424-82202022-07-012214533910.3390/s22145339Multi-Feature Matching GM-PHD Filter for Radar Multi-Target TrackingJin Tao0Defu Jiang1Jialin Yang2Chao Zhang3Song Wang4Yan Han5Laboratory of Array and Information Processing, Hohai University, Nanjing 210098, ChinaLaboratory of Array and Information Processing, Hohai University, Nanjing 210098, ChinaLaboratory of Array and Information Processing, Hohai University, Nanjing 210098, ChinaLaboratory of Array and Information Processing, Hohai University, Nanjing 210098, ChinaLaboratory of Array and Information Processing, Hohai University, Nanjing 210098, ChinaLaboratory of Array and Information Processing, Hohai University, Nanjing 210098, ChinaMulti-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increases exponentially. The Gaussian mixture probability hypothesis density (GM-PHD) filtering based on a random finite set (RFS) provides an effective method to solve multi-target tracking problems without the requirement of explicit data association. However, it is difficult to track targets accurately in real-time with dense clutter and low detection probability. To solve this problem, this paper proposes a multi-feature matching GM-PHD (MFGM-PHD) filter for radar multi-target tracking. Using Doppler and amplitude information contained in radar echo to modify the weights of Gaussian components, the weight of the clutter can be greatly reduced and the target can be distinguished from clutter. Simulations show that the proposed MFGM-PHD filter can improve the accuracy of multi-target tracking as well as the real-time performance with high clutter density and low detection probability.https://www.mdpi.com/1424-8220/22/14/5339radarmulti-target trackingRFSGM-PHDmulti-feature matching |
| spellingShingle | Jin Tao Defu Jiang Jialin Yang Chao Zhang Song Wang Yan Han Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking Sensors radar multi-target tracking RFS GM-PHD multi-feature matching |
| title | Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking |
| title_full | Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking |
| title_fullStr | Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking |
| title_full_unstemmed | Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking |
| title_short | Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking |
| title_sort | multi feature matching gm phd filter for radar multi target tracking |
| topic | radar multi-target tracking RFS GM-PHD multi-feature matching |
| url | https://www.mdpi.com/1424-8220/22/14/5339 |
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