基于最陡下降的稳健LCMV波束形成算法

针对期望信号假定导向矢量与真实导向矢量存在误差时,常规LCMV算法性能急剧下降,提出了一种针对指向误差、阵元位置误差和相位误差的基于最陡下降的稳健LCMV波束形成方法。利用最陡下降法递归搜索最优权矢量和约束导向矢量,避免了常规LCMV算法的矩阵求逆运算和变对角加载时的特征值分解,所需运算量大大降低;又因不属于对角加载,不存在加载值确定问题。仿真结果表明,新方法对期望信号导向矢量的各种误差有很好的稳健性。

Robust LCMV Beamformer Algorithm Based on Steepest Descent Method

Traditional LCMV algorithm suffers performance degradation when there is difference between the actual and presumed array steering vectors.An approach was proposed for robust LCMV beamforming based on recursive steepest descent method in the presence of steering error,array geometry error and sensor phase error.The basic idea is to search the optimal weight vector and the constrained steering vector of the desired signal with the steepest descent method.The algorithm avoids the inverse of the sample covariance matrix and the eigen decomposition of the matrix in the algorithms of variable diagonal loading,so the calculating amount is reduced.And the algorithm does not belong to diagonal loading,so the diagonal level need not to be calculated.Simulations under the errors show that this algorithm has robust performance.