Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration
In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multit...
| Format: | Article |
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| Language: | zho |
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EDP Sciences
2020-08-01
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| Series: | Xibei Gongye Daxue Xuebao |
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| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p797/jnwpu2020384p797.html |
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| collection | DOAJ |
| description | In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased local tracks. In the global processor, in allusion to the global track accuracy degradation introduced by the time offsets of local tracks, the equivalent measurements are firstly constructed based on local tracks by using the inverse Kalman filter. The pseudo-measurement equation of time offset for constant velocity targets is derived and the pseudo-measurement calculation method is presented. Then, the pseudo-measurement based relative time-offset estimation algorithm is presented, by using the recursive least squares estimation (RLSE) and the Kalman filter (KF) to jointly estimate the state in space and time domains, respectively. Finally, a framework of distributed multisensor multitarget tracking with time-offset registration is presented, where the time-varying relative time-offset estimation and compensation, 'equivalent measurement to global track' association, and global track update are included. Simulations for multisensor multitarget tracking in the presence of false alarms and missed detections are conducted, demonstrating that the present algorithm effectively improves the accuracy of fused global tracks. |
| first_indexed | 2024-03-11T20:41:27Z |
| format | Article |
| id | doaj.art-6ba65f610bd24dd3b95256deba5e728f |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-11T20:41:27Z |
| publishDate | 2020-08-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-6ba65f610bd24dd3b95256deba5e728f2023-10-02T01:56:49ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252020-08-0138479780510.1051/jnwpu/20203840797jnwpu2020384p797Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration0123School of Automation, Northwestern Polytechnical UniversitySchool of Automation, Northwestern Polytechnical UniversitySchool of Automation, Northwestern Polytechnical UniversityBeijing Aerospace Automatic Control InstituteIn multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased local tracks. In the global processor, in allusion to the global track accuracy degradation introduced by the time offsets of local tracks, the equivalent measurements are firstly constructed based on local tracks by using the inverse Kalman filter. The pseudo-measurement equation of time offset for constant velocity targets is derived and the pseudo-measurement calculation method is presented. Then, the pseudo-measurement based relative time-offset estimation algorithm is presented, by using the recursive least squares estimation (RLSE) and the Kalman filter (KF) to jointly estimate the state in space and time domains, respectively. Finally, a framework of distributed multisensor multitarget tracking with time-offset registration is presented, where the time-varying relative time-offset estimation and compensation, 'equivalent measurement to global track' association, and global track update are included. Simulations for multisensor multitarget tracking in the presence of false alarms and missed detections are conducted, demonstrating that the present algorithm effectively improves the accuracy of fused global tracks.https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p797/jnwpu2020384p797.htmldistributed track fusionmultitarget trackingequivalent measurementpseudo-measurement equationtime-offset estimation |
| spellingShingle | Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration Xibei Gongye Daxue Xuebao distributed track fusion multitarget tracking equivalent measurement pseudo-measurement equation time-offset estimation |
| title | Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration |
| title_full | Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration |
| title_fullStr | Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration |
| title_full_unstemmed | Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration |
| title_short | Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration |
| title_sort | distributed multisensor multitarget tracking algorithm with time offset registration |
| topic | distributed track fusion multitarget tracking equivalent measurement pseudo-measurement equation time-offset estimation |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2020/04/jnwpu2020384p797/jnwpu2020384p797.html |