阵元非均匀高斯噪声背景下近场声源定位方法  

Maximum Likelihood Localization of Multiple Near-Field Sources in the Presence of Non-uniform Sensor Noise

作　　者：杨一平[1] 张勇[1] 岳金旺 

机构地区：[1]河南大学图像处理与模式识别研究所,河南开封475004

出　　处：《河南大学学报（自然科学版）》2014年第5期595-600,共6页Journal of Henan University:Natural Science

基　　金：国家自然科学基金联合人才培育项目(U1204611);河南省科技厅基础与前沿项目(132300410278);河南大学校内基金资助项目(2012YBZR006)

摘　　要：针对阵元非均匀高斯噪声背景下的近场声源定位问题,研究了最大似然定位方法,并给出克拉美-罗界(CRB),进而为了解决最大似然方法常规求解方法多维参数空间搜索的高运算复杂度问题,提出了基于对数似然函数的步进迭代方法(SML)和近似似然函数法(AML).仿真实验表明,SML方法经过较少的迭代即可收敛,SML方法和AML方法的估计精度较高,均方误差(MSE)在较高信噪比条件下逼近CRB.This paper investigates the maximum presence of unknown nonuniform sensor noise likelihood （ML） Localization of multiple near-field sources in the New closed-form expression for the near-field acoustic Localization Cramer-Rao-Bound （CRB） has been derived. Moreover, two fast algorithms are proposed to lighten computation complexity of conventional maximum likelihood method. The first algorithm is based on an iterative procedure which stepwise concentrates the log-likelihood function with respect to the location of acoustic and the noise nuisance parameters, while the second is a noniterative algorithm that maximizes the derived approximately concentrated log-likelihood function. Simulation results show the stepwise-concentratd ML algorithm （SML） requires only a few iterations to converge and both the SML and the approximately-concentrated ML algorithm （AML） attain a solution close to the derived CRB at high signal-to-noise ratio.

关 键 词：近场源 定位 最大似然估计 计算复杂度 克拉美—罗界 

分 类 号：TN911.7[电子电信—通信与信息系统]