Robust Space-Time Adaptive Track-Before-Detect Algorithm Based on Persymmetry and Symmetric Spectrum

Journal of Beijing Institute of Technology ›› 2024, Vol. 33 ›› Issue (1) : 65 -74.

PDF (1085KB)
Journal of Beijing Institute of Technology ›› 2024, Vol. 33 ›› Issue (1) : 65 -74. DOI: 10.15918/j.jbit1004-0579.2023.090

Robust Space-Time Adaptive Track-Before-Detect Algorithm Based on Persymmetry and Symmetric Spectrum

Author information +
History +
PDF (1085KB)

Abstract

Underwater monopulse space-time adaptive track-before-detect method, which combines space-time adaptive detector (STAD) and the track-before-detect algorithm based on dynamic programming (DP-TBD), denoted as STAD-DP-TBD, can effectively detect low-speed weak targets. However, due to the complexity and variability of the underwater environment, it is difficult to obtain sufficient secondary data, resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm. In this paper, based on the adaptive matched filter (AMF) test and the RAO test, underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum, denoted as PS-AMF-DP-TBD and PS-RAO-DP-TBD, are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array, denoted as P-AMF-DP-TBD and P-RAO-DP-TBD. The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data, but when the secondary data is severely insufficient, the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PS-AMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.

Keywords

space-time adaptive detection / track before detect / robustness / persymmetric property / symmetric spectrum / AMF test / RAO test

Cite this article

Download citation ▾
null. Robust Space-Time Adaptive Track-Before-Detect Algorithm Based on Persymmetry and Symmetric Spectrum. Journal of Beijing Institute of Technology, 2024, 33(1): 65-74 DOI:10.15918/j.jbit1004-0579.2023.090

登录浏览全文

4963

注册一个新账户 忘记密码

References

AI Summary AI Mindmap
PDF (1085KB)

363

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/