Underdetermlned Blind Separation Based on Source Signals’ Number Estimation
基于源信号数目估计的欠定盲分离

This paper gives a new method to estimate the number of source signals and recover them by the characteristics of sparse source signals in underdetermined blind separation. It is well known that source signals can be recovered through the two-step algorithms generally. The first step is to estimate the mixture matrix by K-means clustering algorithm using the sensor signals, and then, the shortest path algorithm is used to recover source signals, whereas, people suppose that the number of source signals is known when they estimate the mixture matrix by the K-means clustering algorithm generally. 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, a new two-step algorithm is proposed, which not only can estimate the number of source signals but also get the mixture matrix instead of K-means algorithm through the characteristics of sensor signals. The last simulation results show the algorithm simply, efficient and good performance.