Robust Adaptive Beamforming Algorithm Based on Complex Gauss–Legendre Integral

Abstract

This study addresses the poor robustness of the current beamforming algorithm for covariance matrix reconstruction and the high computational complexity in the covariance matrix reconstruction process. A robust beamforming algorithm based on the Complex Gauss–Legendre integral is proposed. The method firstly partitions the neighborhood of the interference signal and constructs the interference signal space using the complex Gauss-Legendre integral, then projects the received signal into the interference signal space to filter out the desired signal and complete the reconstruction of the interference noise covariance matrix, before finally correcting the steering vector mismatch using the improved optimal steering vector estimation method. The simulation results show that the method has good robustness and a low sidelobe in the presence of the steering vector mismatch and the presence of array perturbation. Compared with the previous works, the proposed CGL-ISV method provides a better beamforming performance.