一种稳健的特征空间线性约束波束形成器

基于特征空间的线性约束最小方差（eigenspace-based linearly constrained minimum variance,ELCMV）算法是一种稳健的波束形成方法,但其性能只能在一定的指向误差范围内保持稳定,当指向误差较大而使期望信号落在预设波束主瓣边缘时,其性能会严重恶化。对ELCMV算法进行改进,提出一种对零点约束方向和指向误差都具有稳健性的波束形成方法。该方法首先利用矢量旋转对预设导向矢量进行校正,再将校正后的导向矢量向信号子空间投影,最后结合线性约束用线性约束最小方差（linearly constrained minimum variance,LCMV）算法来求解权矢量。计算机仿真证实了该方法的有效性。

A Robust Eigenspace-based Linearly Constrained Beamformer

The eigenspace-based linearly constrained minimum variance（ELCMV） algorithm is a robust beamforming algorithm.However,it couldn＇t deal with large pointing errors near the mainlobe edge.To cure the problem,the paper presents a modified ELCMV algorithm based on the vector-rotating method.The algorithm firstly calibrates the presumed steering vector with a rotated vector,then projects the calibrated steering vector onto the signal subspace,and finally calculates the weight vector with the linearly constrained minimum variance（LCMV） algorithm.Several computer simulations are provided for illustrating the advantages of the proposed algorithm over the conventional ELCMV algorithm.