A Dynamic Programming Track-Before-Detect Algorithm Based on Local Linearization for Non-Gaussian Clutter Background

作     者：ZHENG Daikun WANG Shouyong QIN Xing 

作者机构：Air Force Early Warning Academy 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2016年第25卷第3期

页      面：583-590页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 08[工学] 080203[工学-机械设计及理论] 070105[理学-运筹学与控制论] 0802[工学-机械工程] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China(No.61179014 No.61302193) 

主　　题：Target detection Track-before-detect Dynamic programming Local linearization Non-Gaussian clutter 

摘      要：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 *** 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.