基于嵌套阵列的稀疏表示稳健波束形成方法

针对波束形成中目标方位失配以及噪声加干扰的协方差矩阵非精确重构造成的波束形成方法性能下降的问题，提出一种基于嵌套阵列的稀疏表示稳健波束形成方法。在该方法中，计算嵌套阵的采样协方差矩阵，通过差合作阵处理得到一孔径扩展的虚拟均匀线列阵；基于稀疏表示的方法来估计目标以及干扰的准确方位信息；进一步利用得到的方位信息构造导向矢量，通过最小二乘方法计算干扰信号的精确功率值；最后重构干扰加噪声协方差矩阵，通过波束形成实现干扰抑制。数值仿真表明，所提方法有效提升了干扰加噪声协方差矩阵重构精确度，在不同信噪比和快拍数条件下，输出信噪比都能逼近最优信干噪比，验证了该算法的有效性。

Sparsity-based robust beamforming method using nested array

In order to accurately estimate the target azimuth and recovery noise-plus-interference covariance matrix in beamforming, a robust beamforming method based on sparse representation is proposed under the nested array structure. In the proposed method, firstly, the sampled covariance matrix of the nested array is calculated, and a large-aperture virtual uniform line array is obtained by difference co-array processing. Then, the accurate information of the azimuth of the target is estimated based on a sparse representation method. Using the azimuth information, the power value of the interference signal can be calculated by the least squares method. After obtaining the accurate azimuth information and the power value of the interference, the interference plus noise covariance matrix is further reconstructed, and finally the interference suppression is obtained by beamforming method. Experimental simulation shows that the output Signal to Interference plus Noise Ratio(SINR) can approach the optimal output of SINR under different SNR and snapshots, which verifies the effectiveness of the proposed method.