Mixing Matrix Estimation of Underdetermined Blind Source Separation based on Improved Density Clustering Algorithm
Linyu Wang, Gangyang Hou, Jianhong Xiang · 2019 8th Asia-Pacific Conference on Antennas and Propagation (APCAP) · 2019
In order to solve the problems of the mixing matrix estimation and estimate the number of the of the sources in the underdetermined blind source separation, we proposed a improved density-based clustering algorithm which can first estimate the number of the source signals. In this paper, to avoid clustering algorithm that have problems in initial value selection and local extreme values. We exploit the particle swarm optimization algorithm(PSO) to overcome these problems. The simulation results show that our algorithm can effectively estimate the number of the sources and have a higher accuracy compared with other clustering algorithms.