A blind source separation algorithm based on nonlinear function and simple particle swarm optimization

doi:10.7523/j.issn.2095-6134.2014.04.013

Abstract

Abstract:

A new blind source separation algorithm based on nonlinear function and simple particle swarm optimization was proposed, contraposing limitation problems for the source signal types and gaussian signal numbers. Nonlinear function was used as objective function based on source signal types, and then the simple particle swarm optimization was uesd to optimize the function. Simulation results show that the algorithm achieves the efficient separation for the blind source having various types of source signals and containing two gaussian signals. Compared with other algorithms, the proposed algorithm has fast convergence speed and high separation accuracy.

Key words: various types of source signal, nonlinear function, simple particle swarm optimization, blind source separation

CLC Number:

TN911.7

JIA Zhicheng, WANG Nana, CHEN Lei, ZHANG Yan. A blind source separation algorithm based on nonlinear function and simple particle swarm optimization[J]. , 2014, 31(4): 530-536.

References

[1] Jutten C, Herault J. Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic[J]. Signal Processing, 1991, 24(1):1-10.

[2] Xin J R, Wang J, Chen S H, et al. Research on receiving technology based on blind signal separation for satellite MIMO communication system[J]. Journal of Systemm Simulation, 2013, 3(25):484-488.

[3] Guo L F, Garland M. The use of entropy minimization for the solution of blind source separation problems in image analysis[J]. Pattern Recognition, 2006, 6(39):1066-1073.

[4] Mammone N, La Foresta F, Morabito F C. Automatic artifact rejection from Multichannel scalp EEG by wavelet ICA[J]. Sensors Journal, 2012, 13(2):533-542.

[5] Comon P. Independent component analysis, a new concept?[J]. Signal Processing, 1994, 36:287-314.

[6] Zhang W A ,Liu J H. A blind source separation method based on particle swarm optimization[J].Journal of Taiyuan University of Technology, 2006, 37(2):169-172 (in Chinese). 张文爱, 刘俊豪. 基于粒子群算法的盲源分离算法[J]. 太原理工大学学报, 2006, 37(2):169-172.

[7] Omkar S N, Mudigere D, Narayananaikg, et al. Vector evaluated particle optimization for multi-objective design optimization of composite structures[J]. Computers and Structures, 2008, 86(1/2):1-14.

[8] Hu W, Li Z S. A simper and more effective particle swarm optimization algorithm[J]. Journal of Software, 2007,18(4):861-868 (in Chinese). 胡旺, 李志蜀. 一种更简化而高效的粒子群优化算法[J]. 软件学报, 2007, 18(4):861-868.

[9] Sergio A Cruces-Alvarez. From blind signal extraction to blind instantaneous signal separation[J]. IEEE Transactions on Networks, 2004, 15(4):859-873.

[10] Hacine-Gharbi A, Deriche M, Ravier P, et al. A new histogram-based estimation technique of entropy and mutual information using mean squared error minimization[J]. Computers and Electrical Engineering, 2013, 39:918-933.

[11] Aapo H, Juha K, Erkki O. Independent component analysis[M]. New York:John Wiley & Son, Inc, 2001:36-41.

[12] Aapo Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transaction on Neural Networks, 1999, 10(3):626-634.

[13] Chrisrophe D L, Eric M. Optimal combination of fourth-order cumulant based contrasts for blind separation of noncircular signals[J]. Signal Processing, 2013, 93: 842-855.

[14] Kernnedy J, Eberhart R C. Particle Swarm Optimization[C]//Proceeding of IEEE International Conference on Neutral Networks. Perth, Australia, 1995:1942-1948.

[15] Chen L, Zhang L Y, Guo Y J, et al. Sequential blind signal separation algorithm based on particle swarm optimization[J]. Journal of Tianjin University, 2011, 44(2):174-179 (in Chinese). 陈雷, 张立毅, 郭艳菊, 等. 基于粒子群优化的有序盲信号分离算法[J]. 天津大学学报, 2011, 44(2):174-179.

[16] Shi Y, Eberhart R C. Empirical study of particle swarm optimization[C]//Proceeding IEEE International Conference on Evolutionary Computation. 1997:303-308.