A blind separation algorithm for underdetermined convolutional mixed communication signals based on time–frequency soft mask

Abstract

This paper focuses on the separation for time–frequency (TF) overlapped communication signals received by the sensors. A novel blind separation strategy is proposed to improve the poor performance of signal separation by traditional algorithms for convolutional mixtures in underdetermined cases. Firstly, the number of sources and cluster centers are obtained in the sparse domain by combining the density peak clustering (DPC) with fuzzy c-means (FCM) clustering algorithm; Then the GMM clustering algorithm is applied to calculate the membership degree of the source signal in the mixed signals, so as to construct a TF soft mask matrix to more precisely carry out separation for TF overlapped signals. In this paper, the separation simulations are conducted with the digital modulation signals of 2ASK, BPSK, QPSK, etc. The results show that the algorithm proposed in this paper has better anti-aliasing and anti-noise performance than the comparison algorithms.