Open AccessA Dynamic Programming Track‐Before‐Detect Algorithm Based on Local Linearization for Non‐Gaussian Clutter Background
Author(s) -
Zheng Daikun,
Wang Shouyong,
Qin Xing
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.05.027
Subject(s) - clutter , track (disk drive) , gaussian , algorithm , computer science , linearization , dynamic programming , track before detect , artificial intelligence , radar , nonlinear system , physics , telecommunications , quantum mechanics , operating system
The Dynamic programming track before detect (DP‐TBD) algorithm has been widely used for detection and tracking of weak targets. The selection of the merit function has an immediate influence on the performance of the DP‐TBD. The amplitude merit function is easy to calculate, but the performance of which will decrease in the presence of non‐Gaussian clutter. The likelihood ratio merit function in closed analytical form is difficult to derive under non‐Gaussian background without target signal parameters. To solve this problem, a novel DPTBD algorithm based on local linearization is proposed. Taking maximum of the state conditional probability ratio of the target as the optimal criteria, a recursive integration equation is derived. The equation is locally linearized by Taylor series expansion and a suboptimal multi‐frame test statistic is developed. The calculation of new merit function in the statistic needs only clutter distribution model, and heavy clutter peak can be restrained by making use of clutter distribution characters. So the proposed algorithm can efficiently extract weak target in strong non‐Gaussian clutter. Numerical simulations are provided to assess and compare the performance of the proposed algorithm. It turns out that the proposed algorithm has better detection and tracking performance than the widely used DPTBD algorithm at present and is resilient to various clutter distribution models.