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

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.