Underdetermined blind mixing model recovery using differential evolution and Hough transformation

Underdetermined blind mixing model recovery (UBMMR) is one of the most important steps in separating underdetermined blind sources, which has a direct effect on the recovery accuracy of source signals. A new blind mixing model recovery algorithm is proposed, under the assumption that the sources are sparse. The mixture data observed are first allocated to several clusters using the partitional clustering algorithm based on differential evolution (DE). The cluster centers are amended through Hough transformation to recover the mixing model. The peak clustering problem in Hough transformation is successfully avoided at the same time. Experimental results show that the proposed algorithm has advantages of high robustness and accuracy compared with conventional algorithms.