一种基于稀疏干扰协方差矩阵重构的稳健自适应波束形成算法

Robust adaptive beamforming based on sparse interference covariance matrix reconstruction method

基于Capon谱估计的干扰噪声协方差矩阵重构方法能够消除快拍数据中的期望信号,提高波束形成算法的稳健性,但是当快拍次数较少时Capon谱估计结果不准,重构矩阵存在较大误差而且算法计算量较大。针对上述问题,设计一种基于稀疏干扰协方差矩阵重构的稳健自适应波束形成算法,通过设置方向波动参数对Capon谱估计结果进行修正,利用接收数据矩阵特征值和干扰信号的空域稀疏性,仅在修正后的干扰方向范围内进行重构,从而降低计算量。理论分析和仿真实验表明,新算法在保证对期望信号方向失配稳健的基础上,降低了对快拍次数以及估计误差的敏感性,形成的波束旁瓣电平更低、零陷更深而且零陷得到展宽。

The covariance matrix reconstruction algorithm based on Capon spectrum estimator can eliminate the signal of interest from snapshots, which enhances the robustness of beamforming. However when the number of snapshot is small, the estimation result is inaccurate. Consequently the reconstructed matrix has a big error compared with the ideal one. Besides, the calculation quantity of the original method is huge. To address these problems, a robust adaptive beamforming based on the covariance matrix reconstruction of sparse interference and noise is proposed. The new algorithm sets a parameter to modify the result of Capon spectrum estimator and takes advantage of the interference^s sparsity in spatiality. The eigenvalues of the received data matrix is used to reconstruct the covariance matrix only in the modified directions of interferences, which can reduce the calculation quantity. Theoretical analysis and simulation results show that, the new beamforming algorithm not only has a good robustness against the effect of interest signal＇s DOA mismatch, but also lightens its sensitiveness about the number of snapshots and has a lower sidelobe as well as a deeper and wider nulling.