Adaptive Beam-forming Using a Iterative Adaptive Shrinkage Algorithm

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.