Robust adaptive beamforming based on jointly estimating

Abstract

Traditional adaptive beamformers have robustness just for specific error condition. They suffer performance degradation in the presence of multiple errors such as sample covariance matrix estimation error and steering vector mismatch. In this article, a new robust adaptive beamforming algorithm based on jointly estimating the covariance matrix and steering vector mismatch is proposed to overcome both the problems of sample covariance errors and steering vector mismatch. The theoretical covariance matrix is estimated based on the shrinkage method. Subsequently, the difference between the actual and presumed steering vectors is estimated in the sense of that the output signal-to noise plus interference ratio (SINR) is maximized and then is used to obtain the actual steering vectors. The proposed algorithm is preferable to traditional ones in the condition of multiple errors. Both simulation results and performance analysis are presented that illustrated the effectiveness and superiority of the proposed method.