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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.
Ning Fu,Guangquan Zhao, andXiyuan Peng
"Underdetermined blind mixing model recovery using differential evolution and Hough transformation", Proc. SPIE 7133, Fifth International Symposium on Instrumentation Science and Technology, 71331X (12 January 2009); https://doi.org/10.1117/12.807695
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Ning Fu, Guangquan Zhao, Xiyuan Peng, "Underdetermined blind mixing model recovery using differential evolution and Hough transformation," Proc. SPIE 7133, Fifth International Symposium on Instrumentation Science and Technology, 71331X (12 January 2009); https://doi.org/10.1117/12.807695