Matching Pursuit Based on PSO and Atomic Property
Abstract: As sparse representation of signals has excellent characteristics, it has been applied in several fields of signal processing. But it has a large scale of computing, which hinders its application in practical signal processing. Particle swarm optimization is simple to be realized, and the searching result is good. In this paper, Matching Pursuit is used to realize sparse representation of signals, and particle swarm optimization is used to effectively search the best atom in the process of MP. According to the property of atoms, the improved algorithm is optimized. At last, the simulation results demonstrate the feasibility of the new algorithm.
文章引用: 钱 建 , 赵毅智 , 庄智威 (2014) 基于粒子群优化和原子特性的匹配追踪算法。 计算机科学与应用， 4， 282-287. doi: 10.12677/CSA.2014.411039
 Shi, Y.H. and Eberhart, R.C. (1998) A modified particle swarm optimizer. IEEE International Conference on Evolu-tionary Computation, 4-9 May 1998, Anchorage, 69-73.
 Arthur, P.L. and Philips, C.L. (2003) Voiced/unvoiced speech discrimination in noise using Gabor atomic decomposition. Proceedings of IEEE ICASSP, Hong Kong, 820-828.
 Mallat, S. and Zhang Z. (1993) Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41, 3397-3415.
 Kennedy, J. and Eberhart, R. (2001) Swarm intelligence. Morgan Kaufmann Publishers, San Francisco.
 高鹰, 谢胜利 (2004) 免疫粒子群优化算法. 计算机工程与应用, 6, 47-50.
 高鹰, 谢胜利 (2004) 混沌粒子群优化算法. 计算机科学, 8, 13-15.
 尹忠科, 王建英 (2006) 由图像的稀疏分解重建图像的快速算法. 电子科技大学学报, 4, 448-449.