Abstract:

The underdetermined blind mixing matrix estimation algorithm plays a key role in underdetermined blind source separation. The estimation accuracy of the algorithm directly affects the source signal. With regards to the low stability and the poor estimation accuracy of the current algorithm, a new algorithm based on mixing clustering and mesh density is proposed. The new algorithm clusters the signal data, taking advantages of the clustering algorithm based on artificial bee colony and K-means algorithm to ensure the stability of the clustering results, and improves the estimation accuracy using the method of mesh density. Experiment results show that the new algorithm has the advantages of high stability and estimation accuracy compared with the traditional underdetermined blind mixing matrix estimation algorithm.