一种双层协方差矩阵重构的稳健波束形成算法

A Robust Beamforming Algorithm Based on Double-layer Covariance Matrix Reconstruction

针对采样协方差矩阵中含有信号分量和信号导向矢量失配造成的自适应波束形成器性能下降的问题,提出了一种导向矢量矫正和双层干扰加噪声协方差矩阵重构的稳健波束形成算法。首先,通过子空间投影方法去除接收数据中的干扰和噪声分量来进一步矫正信号导向矢量;然后,利用Capon功率谱初步重构干扰加噪声协方差矩阵;接着,利用干扰子空间的正交性和多重信号分类(Multiple Signal Classification,MUSIC)功率谱进一步精确重构干扰加噪声协方差矩阵;最后,计算出最优权值矢量。仿真结果表明,所提算法在大角度失配和低快拍数条件下具有较好的稳健性。

Considering the problem of the performance degradation of adaptive beam former caused by signal component in sampling covariance matrix and signal steering vector mismatch,the authors proposes a robust beamforming algorithm based on steering vector correction and double-layer covariance matrix reconstruction.Firstly,the interference and noise components in the received data are removed by subspace projection method,and signal steering vector is corrected.Then,the interference-plus-noise covariance matrix is preliminarily reconstructed by Capon power spectrum.After that,the interference-plus-noise covariance matrix is further accurately reconstructed by using the orthogonality of the interference subspace and the Multiple Signal Classification(MUSIC)power spectrum.Finally,the optimal weight vector is calculated.Simulation results demonstrate that the proposed algorithm has better robustness under the conditions of large angle mismatch and low number of snapshots.