迭代自适应收缩加权融合的波束形成方法

Adaptive Beam-forming Using a Iterative Adaptive Shrinkage Algorithm

自适应数字波束形成是通过对阵列接收数据进行加权处理来获得最大的输出信干噪比,对采样协方差矩阵的依赖性较大。针对小样本条件下采样协方差矩阵求逆算法性能下降问题,提出迭代自适应加权融合样本协方差矩阵与先验协方差矩阵的波束形成算法。在估计协方差矩阵时,依据最小均方误差准则计算加权系数,并采用迭代自适应的方式更新先验协方差矩阵。仿真结果表明,所提方法能显著提高小样本条件下的协方差矩阵估计精度,能获得更大的输出信干噪比。

Adaptive beam-forming method can acquire the largest output signal-to interference plus noise ratio by weighted processing of array data, which is largely dependent on the sample covariance matrix. Aiming at the problem of sampling covariance matrix inversion （SMI） algorithm performance decline under the condition of small sample, this paper presents the beamforming algorithm based on iterative adaptive weighted fusion sample covariance matrix and the prior covariance matrix. In the estimation of covariance matrix, weighted coefficient is calculated firstly based on the minimum mean square error criterion, the priori covariance matrix is then updated using the iterative adaptive approach. The simulation results show that the proposed method can significantly improve the estimation accuracy of the covariance matrix under the condition of small sample, and that can obtain higher output signal to interference plus noise ratio.