Coherent Noise Denoising in Beamforming Based on Non-Convex Robust Principal Component Analysis

Abstract

Beamforming maps are often seriously impacted by background noise. Background Noise Subtraction (BNS), Eigenvalue Identification and Subtraction (EIS), and Eigenvalue Identification, Organization and Subtraction (EIOS) have been successively proposed and mainly used to reduce the influence of coherent background noise. In this work, an approach of Robust Principal Component Analysis (RPCA) combined with reference background noise is proposed to effectively suppress coherent background noise. The simulation results demonstrate that the most significant advantage of this method is its robustness to inaccuracies in reference background noise estimation, as it still exhibits good noise suppression performance through RPCA-based denoising. The experimental results also indicate that the Schatten-[Formula: see text] norm consistently outperforms BNS, EIS, EIOS, and nuclear norm-based RPCA in denoising performance, both when overestimating and underestimating the background noise.