Robust Adaptive Beamforming for Distributed Radar Based on Covariance Matrix Reconstruction and Steering Vector Estimation

Abstract

Due to the high dynamic characteristics of the airborne distributed radar, array errors cannot be avoided, thus the performance of the conventional adaptive beamforming methods will deteriorate severely. In order to solve this problem, a robust adaptive beamforming method based on covariance matrix reconstruction and steering Vector estimation is proposed for the airborne distributed radar. In the proposed method, an annulus uncertainty set is used to constrain the interference steering vector, and then the Capon spectrum is integrated on the surface of the region to obtain interference-plus-noise covariance matrix. Afterwards, the iterative robust Capon beamformer is used to obtain a more accurate estimation of the desired signal steering vector. Finally the weight vector is calculated by using the estimated interference-plus-noise covariance matrix and the desired signal steering vector. As the interference-plus-noise covariance matrix is well reconstructed by the annulus uncertainty set and the signal steering vector is calibrated, the proposed method is both robust to array structure errors and signal angle errors compared with the existing methods. The robustness and effectiveness of the proposed algorithm are verified by simulations.