Dynamic programming network for point target detection

EURASIP Journal on Advances in Signal Processing

Research

Hongyan Wei¹  Jingneng Fu² 

[1] Institute of Optics and Electronics, Chinese Academy of Sciences, 610209, Chengdu, China;Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement, Chinese Academy of Sciences, 610209, Chengdu, China;University of Chinese Academy of Sciences, 100049, Beijing, China;Institute of Optics and Electronics, Chinese Academy of Sciences, 610209, Chengdu, China;Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement, Chinese Academy of Sciences, 610209, Chengdu, China;University of Chinese Academy of Sciences, 100049, Beijing, China;Youth Innovation Promotion Association, Chinese Academy of Sciences, 100029, Beijing, China;

关键词: Point target detection;    Dynamic programming;    Dynamic programming ring;    Dynamic programming network;    Merit function diffusion suppression;   

DOI  :  10.1186/s13634-023-01038-7

 received in 2023-02-09, accepted in 2023-06-20,  发布年份 2023

来源: Springer

【 摘 要 】

To improve the efficiency of the dim point target detection based on dynamic programming (DP), this paper proposes a multi-frame target detection method based on a DP ring network (DPRN). In the proposed method, first, the target trajectory is approximated using the piecewise linear function. The velocity space partition DP (VSP-DP) is used to accumulate the merit functions of a target on each piecewise linear trajectory segment to avoid the merit function diffusion in different velocity spaces. In addition, the velocity space matching DP (VSM-DP) is employed to realize the state transition of a target between adjacent piecewise linear trajectory segments. Then, the VSP-DP and VSM-DP are used to construct a DP network (DPN). Second, to suppress the merit function diffusion further, the sequential and reverse DPNs are connected in a head-to-tail manner to form a DPRN, and the merit function of the DPRN is obtained by averaging the merit functions of the sequential and reverse DPNs. Finally, the target trajectory is obtained by tracking the extreme points of the merit functions of the DPRN. The simulation and analysis results show that the proposed DPRN combines the advantages of high detection probability of the high-order DP and high execution efficiency of the first-order DP. The proposed DPRN is suitable for radars and infrared searching and tracking systems.

【 授权许可】

CC BY   
© The Author(s) 2023

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