A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR). In order to solve the problem of radar sampling time var...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/2834 |
| _version_ | 1797615827574325248 |
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| author | Jialin Yang Defu Jiang Jin Tao Yiyue Gao Xingchen Lu Yan Han Ming Liu |
| author_facet | Jialin Yang Defu Jiang Jin Tao Yiyue Gao Xingchen Lu Yan Han Ming Liu |
| author_sort | Jialin Yang |
| collection | DOAJ |
| description | The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR). In order to solve the problem of radar sampling time variation exacerbated by extending the beam dwell time when detecting weak targets, a sector-matching (SM) PHD filter is proposed, which combines the actual radar system with a PHD filter and quantifies the relationship between the beam dwell time, the false alarm rate and the detection probability. The proposed filter divides the scanning area into small sectors to obtain actual multi-target measurement times and rederives the prediction and update steps based on the actual sampling time. Furthermore, a state correction step is added before state extraction. Applying the SM structure to the basic Gaussian mixture PHD (GM-PHD) filter and labeled GM-PHD filter, the simulation results demonstrate that the proposed structure can improve the accuracy of multi-weak-target state estimation in the dense clutter and can continuously generate explicit trajectories. The overall real-time performance of the proposed filter is similar to that of the PHD filter. |
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| id | doaj.art-f681135ecc6a42ef96d5ac0aa289062c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:32:25Z |
| publishDate | 2023-02-01 |
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| spelling | doaj.art-f681135ecc6a42ef96d5ac0aa289062c2023-11-17T07:15:42ZengMDPI AGApplied Sciences2076-34172023-02-01135283410.3390/app13052834A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target TrackingJialin Yang0Defu Jiang1Jin Tao2Yiyue Gao3Xingchen Lu4Yan Han5Ming Liu6The Laboratory of Array and Information Processing, College of Computer and Information, Hohai University, Nanjing 210098, ChinaThe Laboratory of Array and Information Processing, College of Computer and Information, Hohai University, Nanjing 210098, ChinaThe Laboratory of Array and Information Processing, College of Computer and Information, Hohai University, Nanjing 210098, ChinaCollege of Energy and Electrical Engineering, Hohai University, Nanjing 210098, ChinaThe Laboratory of Array and Information Processing, College of Computer and Information, Hohai University, Nanjing 210098, ChinaThe Laboratory of Array and Information Processing, College of Computer and Information, Hohai University, Nanjing 210098, ChinaThe 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, ChinaThe development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR). In order to solve the problem of radar sampling time variation exacerbated by extending the beam dwell time when detecting weak targets, a sector-matching (SM) PHD filter is proposed, which combines the actual radar system with a PHD filter and quantifies the relationship between the beam dwell time, the false alarm rate and the detection probability. The proposed filter divides the scanning area into small sectors to obtain actual multi-target measurement times and rederives the prediction and update steps based on the actual sampling time. Furthermore, a state correction step is added before state extraction. Applying the SM structure to the basic Gaussian mixture PHD (GM-PHD) filter and labeled GM-PHD filter, the simulation results demonstrate that the proposed structure can improve the accuracy of multi-weak-target state estimation in the dense clutter and can continuously generate explicit trajectories. The overall real-time performance of the proposed filter is similar to that of the PHD filter.https://www.mdpi.com/2076-3417/13/5/2834multi-target trackingradarrandom finite setssampling time varietyweak targets |
| spellingShingle | Jialin Yang Defu Jiang Jin Tao Yiyue Gao Xingchen Lu Yan Han Ming Liu A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking Applied Sciences multi-target tracking radar random finite sets sampling time variety weak targets |
| title | A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking |
| title_full | A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking |
| title_fullStr | A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking |
| title_full_unstemmed | A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking |
| title_short | A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking |
| title_sort | sector matching probability hypothesis density filter for radar multiple target tracking |
| topic | multi-target tracking radar random finite sets sampling time variety weak targets |
| url | https://www.mdpi.com/2076-3417/13/5/2834 |
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