Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only app...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/9/1469 |
| _version_ | 1798005409392361472 |
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| author | Qian Zhang Taek Lyul Song |
| author_facet | Qian Zhang Taek Lyul Song |
| author_sort | Qian Zhang |
| collection | DOAJ |
| description | In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood function with a Gaussian mixture instead of a single Gaussian distribution. Besides, the target birth model of the GMM-PHD filter is assumed to be partially uniform instead of a Gaussian mixture. Simulation results show that the proposed filter outperforms the GM-PHD filter embedded with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). |
| first_indexed | 2024-04-11T12:38:59Z |
| format | Article |
| id | doaj.art-828da24273dd415cb28c528297208e08 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:38:59Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-828da24273dd415cb28c528297208e082022-12-22T04:23:33ZengMDPI AGSensors1424-82202016-09-01169146910.3390/s16091469s16091469Improved Bearings-Only Multi-Target Tracking with GM-PHD FilteringQian Zhang0Taek Lyul Song1Department of Electronic Systems Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, KoreaDepartment of Electronic Systems Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, KoreaIn this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood function with a Gaussian mixture instead of a single Gaussian distribution. Besides, the target birth model of the GMM-PHD filter is assumed to be partially uniform instead of a Gaussian mixture. Simulation results show that the proposed filter outperforms the GM-PHD filter embedded with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF).http://www.mdpi.com/1424-8220/16/9/1469nonlinear estimationbearings-only measurementmulti-target trackingGaussian mixture measurementspassive sensor |
| spellingShingle | Qian Zhang Taek Lyul Song Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering Sensors nonlinear estimation bearings-only measurement multi-target tracking Gaussian mixture measurements passive sensor |
| title | Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering |
| title_full | Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering |
| title_fullStr | Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering |
| title_full_unstemmed | Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering |
| title_short | Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering |
| title_sort | improved bearings only multi target tracking with gm phd filtering |
| topic | nonlinear estimation bearings-only measurement multi-target tracking Gaussian mixture measurements passive sensor |
| url | http://www.mdpi.com/1424-8220/16/9/1469 |
| work_keys_str_mv | AT qianzhang improvedbearingsonlymultitargettrackingwithgmphdfiltering AT taeklyulsong improvedbearingsonlymultitargettrackingwithgmphdfiltering |