TY - GEN
T1 - A Multi-Sensor Multi-Target Tracker Based on Labeled MS-CPHD Filter
AU - Zhang, Zhiguo
AU - Sun, Jinping
AU - Lu, Xiaoke
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The multi-sensor cardinalized probability hypothesis density (MS-CPHD) filter based on the random finite set (RFS) have been developed in the literature for multi-sensor multitarget tracking. However, this filter is not strictly a multi-target tracker as it cannot estimate identities of individual target states. To form the target tracks, a multiple target tracker based on the MS-CPHD filter is given in this paper. Specifically, in the Gaussian mixture recursion of the MS-CPHD filter, each Gaussian component is identify identified with a unique label for separating different targets. Then the target tracks can be determined from the calculation of the Gaussian component with a corresponding label. Furthermore, we also propose a track management mechanism to determine the creation, maintenance, and termination of tracks. Numerical results from simulations show that, our proposed method can obtain target tracks and has higher filtering accuracy compared with the original MS-CPHD filter, especially in scenarios with high clutter intensity.
AB - The multi-sensor cardinalized probability hypothesis density (MS-CPHD) filter based on the random finite set (RFS) have been developed in the literature for multi-sensor multitarget tracking. However, this filter is not strictly a multi-target tracker as it cannot estimate identities of individual target states. To form the target tracks, a multiple target tracker based on the MS-CPHD filter is given in this paper. Specifically, in the Gaussian mixture recursion of the MS-CPHD filter, each Gaussian component is identify identified with a unique label for separating different targets. Then the target tracks can be determined from the calculation of the Gaussian component with a corresponding label. Furthermore, we also propose a track management mechanism to determine the creation, maintenance, and termination of tracks. Numerical results from simulations show that, our proposed method can obtain target tracks and has higher filtering accuracy compared with the original MS-CPHD filter, especially in scenarios with high clutter intensity.
KW - Gaussian mixture recursion
KW - MS-CPHD filter
KW - random finite set
KW - target tracks
UR - https://www.scopus.com/pages/publications/85123474115
U2 - 10.1109/CISP-BMEI53629.2021.9624356
DO - 10.1109/CISP-BMEI53629.2021.9624356
M3 - 会议稿件
AN - SCOPUS:85123474115
T3 - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
BT - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
A2 - Li, Qingli
A2 - Wang, Lipo
A2 - Wang, Yan
A2 - Li, Wenwu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Y2 - 23 October 2021 through 25 October 2021
ER -