典型文献
DOA estimation of incoherently distributed sources using importance sampling maximum likelihood
文献摘要：
In this paper, an importance sampling maximum likeli-hood (ISML) estimator for direction-of-arrival (DOA) of incohe-rently distributed (ID) sources is proposed. Starting from the maxi-mum likelihood estimation description of the uniform linear array (ULA), a decoupled concentrated likelihood function (CLF) is pre-sented. A new objective function based on CLF which can obtain a closed-form solution of global maximum is constructed according to Pincus theorem. To obtain the optimal value of the objective function which is a complex high-dimensional integral, we propose an importance sampling approach based on Monte Carlo random calculation. Next, an importance function is de-rived, which can simplify the problem of generating random vec-tor from a high-dimensional probability density function (PDF) to generate random variable from a one-dimensional PDF. Com-pared with the existing maximum likelihood (ML) algorithms for DOA estimation of ID sources, the proposed algorithm does not require initial estimates, and its performance is closer to Cramer-Rao lower bound (CRLB). The proposed algorithm performs bet-ter than the existing methods when the interval between sources to be estimated is small and in low signal to noise ratio (SNR) scenarios.
文献关键词：
中图分类号：
作者姓名：
WU Tao;DENG Zhenghong;HU Xiaoxiang;LI Ao;XU Jiwei
作者机构：
School of Automation,Northwestern Polytechnical University,Xi'an 710072,China;Equipment Management and UAV College,Air Force Engineering University,Xi'an 710051,China;School of Information Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710061,China
文献出处：
引用格式：
[1]WU Tao;DENG Zhenghong;HU Xiaoxiang;LI Ao;XU Jiwei-.DOA estimation of incoherently distributed sources using importance sampling maximum likelihood)[J].系统工程与电子技术（英文版）,2022(04):845-855
A类：
incoherently,likeli,ISML,incohe,Pincus
B类：
DOA,estimation,distributed,sources,using,importance,sampling,maximum,likelihood,In,this,paper,estimator,direction,arrival,ID,proposed,Starting,from,description,uniform,linear,array,ULA,decoupled,concentrated,function,CLF,pre,sented,new,objective,which,can,obtain,closed,solution,global,constructed,according,theorem,To,optimal,value,complex,high,dimensional,integral,approach,Monte,Carlo,random,calculation,Next,rived,simplify,problem,generating,vec,probability,density,PDF,generate,variable,one,Com,pared,existing,algorithms,does,not,require,initial,estimates,its,performance,closer,Cramer,Rao,lower,bound,CRLB,performs,than,methods,when,interval,between,estimated,small,signal,noise,ratio,SNR,scenarios
AB值：
0.519201
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