A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms
The estimation of the target number and individual tracks are two major tasks in multi-target tracking. The main shortcoming of traditional tracking methods is the cumbersome data association between measurements and targets. The cardinalized probability hypothesis density filter (CPHD) proposed in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/2/251 |
| _version_ | 1797342642526224384 |
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| author | Qi Jiang Rui Wang Libin Dou Longxiang Jiao Cheng Hu |
| author_facet | Qi Jiang Rui Wang Libin Dou Longxiang Jiao Cheng Hu |
| author_sort | Qi Jiang |
| collection | DOAJ |
| description | The estimation of the target number and individual tracks are two major tasks in multi-target tracking. The main shortcoming of traditional tracking methods is the cumbersome data association between measurements and targets. The cardinalized probability hypothesis density filter (CPHD) proposed in recent years can achieve the requirement for multitarget tracking. This kind of filter jointly estimates the cardinality distribution and the posterior density, which can achieve a more stable estimate of the target number. However, targets with complex micro-Doppler signatures (drones, birds, etc.) may generate target-dependent false alarms, which is contrary to the traditional uniform distribution assumption. In this case, the estimates of traditional CPHD filter will suffer from the abnormal transfer of PHD mass, causing the degradation of filtering performance. This paper studies the individual tracking of group targets with an improved GM-CPHD filter. First, the target-dependent false alarms are modeled with a general independent and identically distributed (I.I.D.) cluster process. Second, the update equations of cardinality and PHD density in target-dependent false alarms are derived. Finally, a practical solution using the Gaussian mixture method is proposed. The effectiveness of the proposed filter is verified by the simulation and experimental results. |
| first_indexed | 2024-03-08T10:36:13Z |
| format | Article |
| id | doaj.art-3bb05216fab04a68919bd435ac8489f9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T10:36:13Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3bb05216fab04a68919bd435ac8489f92024-01-26T18:16:42ZengMDPI AGRemote Sensing2072-42922024-01-0116225110.3390/rs16020251A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False AlarmsQi Jiang0Rui Wang1Libin Dou2Longxiang Jiao3Cheng Hu4Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaRadar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaThe estimation of the target number and individual tracks are two major tasks in multi-target tracking. The main shortcoming of traditional tracking methods is the cumbersome data association between measurements and targets. The cardinalized probability hypothesis density filter (CPHD) proposed in recent years can achieve the requirement for multitarget tracking. This kind of filter jointly estimates the cardinality distribution and the posterior density, which can achieve a more stable estimate of the target number. However, targets with complex micro-Doppler signatures (drones, birds, etc.) may generate target-dependent false alarms, which is contrary to the traditional uniform distribution assumption. In this case, the estimates of traditional CPHD filter will suffer from the abnormal transfer of PHD mass, causing the degradation of filtering performance. This paper studies the individual tracking of group targets with an improved GM-CPHD filter. First, the target-dependent false alarms are modeled with a general independent and identically distributed (I.I.D.) cluster process. Second, the update equations of cardinality and PHD density in target-dependent false alarms are derived. Finally, a practical solution using the Gaussian mixture method is proposed. The effectiveness of the proposed filter is verified by the simulation and experimental results.https://www.mdpi.com/2072-4292/16/2/251CPHD filterGM-CPHD filtergroup target trackingtarget-dependent false alarms |
| spellingShingle | Qi Jiang Rui Wang Libin Dou Longxiang Jiao Cheng Hu A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms Remote Sensing CPHD filter GM-CPHD filter group target tracking target-dependent false alarms |
| title | A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms |
| title_full | A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms |
| title_fullStr | A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms |
| title_full_unstemmed | A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms |
| title_short | A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms |
| title_sort | gaussian mixture cphd filter for multi target tracking in target dependent false alarms |
| topic | CPHD filter GM-CPHD filter group target tracking target-dependent false alarms |
| url | https://www.mdpi.com/2072-4292/16/2/251 |
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