Eigenanalysis-based interference suppression for robust adaptive beamforming

Abstract

Weak target detection is quite a difficult problem in the presence of interference and noise. Generally, adaptive beamforming is an efficient means of interference suppression. However, conventional adaptive beamforming, e.g. MVDR may degrade significantly due to mismatch, especially in many practical applications where the target-free covariance matrix is unavailable. Eigenanalysis always plays a key role in interference nulling, while convex optimization has now emerged as a powerful tool in signal processing because of its foundational nature and potential ability. In this paper, a robust adaptive interference suppression method is presented, and then the worst-case performance optimization is implemented on the eigenanalysis-based re-constructed covariance matrix. Simulation and experimental results show that the proposed method can efficiently suppress the interferences and then be for robust adaptive beamforming.