Intelligent particle blind source separation research

Article type: Research Article

Authors: Liu, Qiong | Peng, Hao | Wang, Haitao

Affiliations: School of Information Science and Engineering, Hunan International Economics University, 410205 Changsha, Hunan, China | E-mail: [email protected]

Abstract: A new sequential blind source separation algorithm is proposed based on intelligent single particle optimization. The signal variability is used as the objective function and the separation vector is transformed by using the spherical coordinate transform method. The intelligent single particle optimization is used for solving the objective function. By de-correlation method, the separated source signal component is removed from the mixed signals, and source signal could be separated out respectively according to the decreasing order of the signal variability. Simulation results show that the proposed separation algorithm can realize sequential separation of source signal efficiently and the separation precision is very high.

Keywords: Blind information, signal separation, signal variability, intelligent single particle, sequential separation

DOI: 10.3233/JCM-180791

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 18, no. 2, pp. 319-327, 2018