Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

doi:10.23919/JSEE.2023.000134

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 780-792.doi: 10.23919/JSEE.2023.000134

• CONTROL THEORY AND APPLICATION • Previous Articles

Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

Changwen DING¹(), Di ZHOU¹^,*(), Xinguang ZOU²(), Runle DU³(), Jiaqi LIU³()

¹ School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
² School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
³ National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing 100076, China

Received:2022-08-26 Online:2024-06-18 Published:2024-06-19
Contact: Di ZHOU E-mail:dcwhit@163.com;zhoud@hit.edu.cn;xgzou@hit.edu.cn;jenniferdu@126.com;ljq006@vip.sina.com
About author:
DING Changwen was born in 1995. He received his B.E. degree in automatic control from Harbin Institute of Technology, Weihai, China, in 2018. He is currently pursuing his Ph.D. degree in control science and engineering at Harbin Institute of Technology, Harbin, China. His research interests include nonlinear control, target tracking, and guidance and control of flight vehicles. E-mail: dcwhit@163.com

ZHOU Di was born in 1969. He received his B.E. and Ph.D. degrees in automatic control from Harbin Institute of Technology, Harbin, China, in 1991 and 1996, respectively. He is a professor in School of Astronautics, Harbin Institute of Technology. His research interests include nonlinear control, nonlinear filtering, and guidance and control of flight vehicles. E-mail: zhoud@hit.edu.cn

ZOU Xinguang was born in 1975. He received his B.E., M.E., and Ph.D. degrees in communication engineering from Harbin Institute of Technology, Harbin, China, in 1997, 1999 and 2002, respectively. He is currently working as an associated professor in School of Electronics and Information Engineering, Harbin Institute of Technology. His research interests include target tracking, navigation and guidance. E-mail: xgzou@hit.edu.cn

DU Runle was bron in 1975. She received her master degree in engineering from Beijing Astronautic Institute of Long March Vehicle, in 2008. Now she is a research fellow in National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics. Her research interests include navigation guidance and control, data fusion, and tracking on maneuvering targets. E-mail: jenniferdu@126.com

LIU Jiaqi was born in 1963. He received his doctor’s degree of circuit and systems from Beijing University of Aeronautics and Astronautics. Currently he serves as the vice director and leading research fellow of National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics. His research interests include radar signal processing, target characteristic study, and target recognition. E-mail: ljq006@vip.sina.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61773142).

Abstract

Abstract:

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’ information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Key words: multitarget tracking, probability hypothesis density (PHD) filter, sharply maneuvering targets, multiple model, adaptive birth intensity estimation

Changwen DING, Di ZHOU, Xinguang ZOU, Runle DU, Jiaqi LIU. Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation[J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 780-792.

Figures/Tables 9

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Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

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