An Underdetermined Blind Separation Algorithm Based on Fuzzy Clustering

In underdetermined blind separation, the ´two-step approach´ is often adopted, which depends on source signals´ sparse representation. The first step is to estimate the mixture matrix by K-mean clustering algorithm using the sensor signals; and in the second step, the shortest-path algorithm is used to recover source signals. Generally, people suppose that the number of source signals is known when they estimate the mixture matrix by the K-mean clustering algorithm. In fact, the number of source signals is unknown or blind, so it is very important to estimate the number of source signals. In this paper, it gives a novel underdetermined blind separation algorithm based on fuzzy clustering, which can accurately estimate the number of sources and the mixture matrix respectively, by which source signals can be reconstructed. The last simulations show the good performance of the paper´s algorithm.