TY - GEN
T1 - A CPHD Filter Based on EM Star-Convex Random Hypersurface Model for Multiple Extended Targets
AU - Li, Weijie
AU - Sun, Jinping
AU - Feng, Fuyuan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Aiming at the problem of tracking multiple extended targets with irregular shapes in complex scenes, this paper presents a Cardinalized Probability Hypothesis Density (CPHD) filter built on EM iteration and Star-Convex Random Hypersurface Model (SRHM). Firstly, based on the theory of Finite Set Statistics (FISST), the Bayesian filtering framework for multiple extended targets is established by using the CPHD filter. Then, SRHM is adopted to describe the measurement source distribution of the star-convex extended target, and Unscented Transform is used to embed the CPHD filtering process. In addition, the incorporation of EM iteration significantly alleviates the dependence of the SRHM-CPHD filter on the RHM prior. The simulation results show that the tracking performance of the proposed EMSRHM-CPHD filter is better than that of the SRHM-CPHD filter, and the parameter estimation of the extended targets is more accurate.
AB - Aiming at the problem of tracking multiple extended targets with irregular shapes in complex scenes, this paper presents a Cardinalized Probability Hypothesis Density (CPHD) filter built on EM iteration and Star-Convex Random Hypersurface Model (SRHM). Firstly, based on the theory of Finite Set Statistics (FISST), the Bayesian filtering framework for multiple extended targets is established by using the CPHD filter. Then, SRHM is adopted to describe the measurement source distribution of the star-convex extended target, and Unscented Transform is used to embed the CPHD filtering process. In addition, the incorporation of EM iteration significantly alleviates the dependence of the SRHM-CPHD filter on the RHM prior. The simulation results show that the tracking performance of the proposed EMSRHM-CPHD filter is better than that of the SRHM-CPHD filter, and the parameter estimation of the extended targets is more accurate.
KW - CPHD filter
KW - EM iteration
KW - multiple extended targets tracking
KW - star-convex random hypersurface model
UR - https://www.scopus.com/pages/publications/85146250536
U2 - 10.1109/CISP-BMEI56279.2022.9979934
DO - 10.1109/CISP-BMEI56279.2022.9979934
M3 - 会议稿件
AN - SCOPUS:85146250536
T3 - Proceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
BT - Proceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
A2 - Chen, Xin
A2 - Cao, Lin
A2 - Li, Qingli
A2 - Wang, Yan
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
Y2 - 5 November 2022 through 7 November 2022
ER -