Blind source separation method based on blind compression transformation under impulsive noise

When strong impulsive noise exists in observed signals, the existing blind source separation (BSS) methods are less accurate or even ineffective, and the parameter settings of existing noise suppression methods rely on prior knowledge to ensure good performance, thus cannot be applied to the BSS problem. To address the above problems, this paper proposes a BSS method that can still achieve effective signal separation under impulsive noise. A new compression transformation function that does not depend on any prior knowledge is designed to process the observed signals, named the blind compression transformation (BCT) function. The received observed signals are processed using the proposed BCT, and then the short-time Fourier transformation (STFT) is performed on the processed observed signals to complete the signal separation in the frequency domain. An adaptive energy correlation permutation algorithm based on frequency correction is designed to solve the permutation ambiguity in the frequency domain, and the inverse short-time Fourier transformation (ISTFT) is performed to achieve the source signals recovery. In general, the proposed method can suppress impulsive noise without any prior knowledge and solve permutation ambiguity without empirically setting threshold, which achieves effective signal separation under impulsive noise. The superior performance of our proposed method is evaluated through numerical simulations for the considered scenarios.