Tracking Algorithms Aided by the Pose of Target
The traditional target tracking algorithms have utilized the information on the target position. With the development of radar high-resolution technology, it is possible to obtain the pose of target. In this paper, two target tracking algorithms aided by the pose of target are proposed. First, the p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8604001/ |
| _version_ | 1818557677622525952 |
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| author | Dai Liu Yongbo Zhao Baoqing Xu |
| author_facet | Dai Liu Yongbo Zhao Baoqing Xu |
| author_sort | Dai Liu |
| collection | DOAJ |
| description | The traditional target tracking algorithms have utilized the information on the target position. With the development of radar high-resolution technology, it is possible to obtain the pose of target. In this paper, two target tracking algorithms aided by the pose of target are proposed. First, the pose of the target is estimated in the real time by the high-resolution range profile, and then, the pose is added to the target measurement equation. Because the relationship between the pose and the motion parameters of the targets is nonlinear, the extended Kalman filter algorithm aided by the pose of target (Pose-EKF) and the unscented Kalman filter algorithm aided by the pose of target (Pose-UKF) are proposed. The results of simulation demonstrate that compared with the traditional extended Kalman filter algorithm (EKF) and the traditional Unscented Kalman filter algorithm (UKF), the proposed algorithm can greatly improve the target tracking accuracy (position precision and velocity precision) and the convergence speed. The pose measurement error has a little effect on the tracking performance. The difference in the tracking accuracy between Pose-EKF and Pose-UKF is very little. But the Pose-EKF is better than Pose-UKF in terms of computation time, but Pose-EKF fails and Pose-UKF is effective when the pose is critical. |
| first_indexed | 2024-12-14T00:02:42Z |
| format | Article |
| id | doaj.art-4ecda9cb3f264bd8a0352311f3fa4640 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:02:42Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4ecda9cb3f264bd8a0352311f3fa46402022-12-21T23:26:15ZengIEEEIEEE Access2169-35362019-01-0179627963310.1109/ACCESS.2019.28909818604001Tracking Algorithms Aided by the Pose of TargetDai Liu0https://orcid.org/0000-0001-7794-6468Yongbo Zhao1Baoqing Xu2https://orcid.org/0000-0001-7266-6491National Laboratory of Radar Signal Processing, Xidian University, Xi’an, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an, ChinaThe traditional target tracking algorithms have utilized the information on the target position. With the development of radar high-resolution technology, it is possible to obtain the pose of target. In this paper, two target tracking algorithms aided by the pose of target are proposed. First, the pose of the target is estimated in the real time by the high-resolution range profile, and then, the pose is added to the target measurement equation. Because the relationship between the pose and the motion parameters of the targets is nonlinear, the extended Kalman filter algorithm aided by the pose of target (Pose-EKF) and the unscented Kalman filter algorithm aided by the pose of target (Pose-UKF) are proposed. The results of simulation demonstrate that compared with the traditional extended Kalman filter algorithm (EKF) and the traditional Unscented Kalman filter algorithm (UKF), the proposed algorithm can greatly improve the target tracking accuracy (position precision and velocity precision) and the convergence speed. The pose measurement error has a little effect on the tracking performance. The difference in the tracking accuracy between Pose-EKF and Pose-UKF is very little. But the Pose-EKF is better than Pose-UKF in terms of computation time, but Pose-EKF fails and Pose-UKF is effective when the pose is critical.https://ieeexplore.ieee.org/document/8604001/Target trackingpose measurethe extended Kalman filterthe unscented Kalman filterthe high resolution range profile |
| spellingShingle | Dai Liu Yongbo Zhao Baoqing Xu Tracking Algorithms Aided by the Pose of Target IEEE Access Target tracking pose measure the extended Kalman filter the unscented Kalman filter the high resolution range profile |
| title | Tracking Algorithms Aided by the Pose of Target |
| title_full | Tracking Algorithms Aided by the Pose of Target |
| title_fullStr | Tracking Algorithms Aided by the Pose of Target |
| title_full_unstemmed | Tracking Algorithms Aided by the Pose of Target |
| title_short | Tracking Algorithms Aided by the Pose of Target |
| title_sort | tracking algorithms aided by the pose of target |
| topic | Target tracking pose measure the extended Kalman filter the unscented Kalman filter the high resolution range profile |
| url | https://ieeexplore.ieee.org/document/8604001/ |
| work_keys_str_mv | AT dailiu trackingalgorithmsaidedbytheposeoftarget AT yongbozhao trackingalgorithmsaidedbytheposeoftarget AT baoqingxu trackingalgorithmsaidedbytheposeoftarget |