A Robust Adaptive Beamforming Algorithm Based on Diagonal Loading and Covariance Matrix Reconstruction

Abstract

Traditional adaptive beamformers often exhibit performance degradation under model mismatches or limited sample scenarios. To address these limitations, this paper proposes a robust adaptive beamforming algorithm that integrates diagonal loading and interference-plus-noise covariance matrix (INCM) reconstruction. Firstly, a dynamic mechanism is developed to determine the diagonal loading factor based on the signal-to-noise ratio (SNR), eigenvalue dispersion, and the number of snapshots. Secondly, the INCM is reconstructed by integrating the optimized spatial power spectrum over the non-target signal region. Finally, a steering vector solution method based on a two-step optimization process is derived and formulated to optimize the steering vector of the desired signal (DS) within its angular neighborhood. Simulation results demonstrate that the proposed algorithm effectively handles challenging scenarios, such as limited snapshots, large angular deviations, and multiple steering vector mismatches. It also consistently outperforms the traditional Capon beamformer and representative INCM reconstruction-based algorithms.