Single Channel Blind Source Number Estimation Algorithm Based on Source Information Entropy Minimization.

The problem of source number estimation was a key issue in blind source separation (BSS), because the number of sources directly affected the effect of BSS. To solve this problem, this study proposed a single-channel blind source number estimation algorithm that took the information entropy as the statistical evaluation index, and used the information entropy to measure the information quantity of the source signal to determine the source number. To calculate the information entropy of the estimated source signals, firstly, the Gaussian mixture model (GMM) was used to fit their distributions. Secondly, samples obeying the target distribution were sampled and the entropy was calculated based on the Markov chain Monte Carlo (MCMC) algorithm. Finally, the source number was obtained by minimizing the average information entropy of the source signal. A series of experiments based on simulation data and real communication data showed that the proposed algorithm had strong robustness and could estimate the number of sources with 94% accuracy, thus verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]