Eigenanalysis-based adaptive beamforming using worst-case performance optimization

Robust adaptive beamforming is an efficient way for both spatial filtering and weak target detection in the presence of interference and noise,particularly in oceanic engineering where mismatch most often degrades the performance of conventional adaptive beamforming.In the proposed method,the covariance matrix is re-constructed based on eigenanalysis,using a more stable and practical criterion.Convex optimization is implemented on the reconstructed covariance matrix to further improve the robustness.Numerical simulations and experimental results show that the proposed method can efficiently suppress the interference and robustly detect the weak target under the condition that average sensors' position error is less than 40%of incident signal wavelength.The proposed robust adaptive beamforming has higher output signal to interference plus noise ratio,and is less sensitive to the diagonal loading factor.