An Approximate Maximum Likelihood Algorithm for Target Localization in Multistatic Passive Radar  

作　　者：WANG Jun QIN Zhaotao GAO Fei WEI Shaoming 

机构地区：[1]School of Electronics and Information Engineering,Beihang University

出　　处：《Chinese Journal of Electronics》2019年第1期195-201,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61501012,No.61671035,No.61771027);the Foundation of ATR Key Laboratory(No.6142503010202)

摘　　要：This paper addresses the problem of target localization using Bistatic range(BR) measurements in a distributed multistatic passive radar system. The rangebased positioning technique employs multiple transmitterreceiver pairs, which provide separate BR measurements.Based on the Maximum likelihood(ML) function,an efficient algebraic Approximate maximum likelihood(AML) algorithm for single target localization is proposed.The closed-form AML solution has neither initial condition requirements nor convergence difficulty. Simulations are included to compare its performance to that of the CramerRao lower bound(CRLB) and the Two-step Weighted least squares(TS-WLS) algorithm. The proposed method is shown to be able to achieve the CRLB accuracy under Gaussian measurement noise. It is more robust to noise than the TS-WLS method, and presents relative insensitivity to target-sensor geometry.This paper addresses the problem of target localization using Bistatic range(BR) measurements in a distributed multistatic passive radar system. The rangebased positioning technique employs multiple transmitterreceiver pairs, which provide separate BR measurements.Based on the Maximum likelihood(ML) function,an efficient algebraic Approximate maximum likelihood(AML) algorithm for single target localization is proposed.The closed-form AML solution has neither initial condition requirements nor convergence difficulty. Simulations are included to compare its performance to that of the CramerRao lower bound(CRLB) and the Two-step Weighted least squares(TS-WLS) algorithm. The proposed method is shown to be able to achieve the CRLB accuracy under Gaussian measurement noise. It is more robust to noise than the TS-WLS method, and presents relative insensitivity to target-sensor geometry.

关 键 词：BISTATIC range (BR)measurement Multistatic passive radar Target localization APPROXIMATE MAXIMUM LIKELIHOOD (AML)algorithm MAXIMUM LIKELIHOOD (ML)function 

分 类 号：TN[电子电信]