阵列模型误差条件下直接定位性能分析及偏差修正方法

Performance Analysis and Deviation Correction Method of Direct Position Determination in the Presence of Array Modeling Errors

相比于常规的“参数估计+位置解算”两步定位模式,直接定位(DPD)算法具有定位精度高、分辨能力强等诸多优势.但是,DPD算法的性能受到阵列模型误差的影响.本文通过一阶Taylor级数展开,定量推导出模型误差条件下基于多重信号分类直接定位算法(MUSIC-DPD)的定位误差,从定位误差的表达式中可以发现辐射源的真实位置和MUSIC-DPD所得的有偏位置估计之间存在一种非线性关系,但这关系在实际条件下无法精确表示.为此,本文提出一种基于多层感知器(MLP)神经网络的直接定位偏差修正方法,该方法能够直接学习由阵列模型误差引起的定位偏差的规律,有效地修正由阵列模型误差导致的直接定位偏差.

Compared with the conventional two-step(including parameter estimation and position solution)localization mode,direct position determination(DPD)algorithm has more advantages,such as higher position estimation accuracy,strong resolution capability,etc.However,the performance of DPD algorithm is affected by the array modeling errors.In this paper,the localization deviation for multiple signal classification(MUSIC)-based DPD algorithm is derived quantitatively by using first-order Taylor expansion in the presence of array modeling errors.The result demonstrates that there is a nonlinear relationship between the true location of the radiation source and biased position estimation obtained from MUSIC-DPD,but this relationship cannot be accurately expressed in practice.To solve this problem,we propose the use of a multilayer perceptron(MLP)neural network(NN)for localization deviation correction of DPD.This method can effectively correct the localization deviation of DPD caused by array modeling errors through adaptive learning of NN.