Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptivel...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/10/1666 |
| _version_ | 1798037949808377856 |
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| author | Feng Yang Yongqi Wang Hao Chen Pengyan Zhang Yan Liang |
| author_facet | Feng Yang Yongqi Wang Hao Chen Pengyan Zhang Yan Liang |
| author_sort | Feng Yang |
| collection | DOAJ |
| description | In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy. |
| first_indexed | 2024-04-11T21:33:33Z |
| format | Article |
| id | doaj.art-f2172d68133c4d5e83cc873c3be63e9f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:33:33Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f2172d68133c4d5e83cc873c3be63e9f2022-12-22T04:01:50ZengMDPI AGSensors1424-82202016-10-011610166610.3390/s16101666s16101666Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target TrackingFeng Yang0Yongqi Wang1Hao Chen2Pengyan Zhang3Yan Liang4School of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSouthwest China Research Institute of Electronic Equipment (SWIEE), Chengdu 610036, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaIn this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.http://www.mdpi.com/1424-8220/16/10/1666multi-target trackingmulti-target state and track extractionGMPHD filter |
| spellingShingle | Feng Yang Yongqi Wang Hao Chen Pengyan Zhang Yan Liang Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking Sensors multi-target tracking multi-target state and track extraction GMPHD filter |
| title | Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking |
| title_full | Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking |
| title_fullStr | Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking |
| title_full_unstemmed | Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking |
| title_short | Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking |
| title_sort | adaptive collaborative gaussian mixture probability hypothesis density filter for multi target tracking |
| topic | multi-target tracking multi-target state and track extraction GMPHD filter |
| url | http://www.mdpi.com/1424-8220/16/10/1666 |
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