Liu Kai, Li Hui, Dai Xu-chu, Xu Pei-xia. A Novel Denoising Algorithm for Contaminated Chaotic Signals[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1849-1852. doi: 10.3724/SP.J.1146.2007.00043
Citation:
Liu Kai, Li Hui, Dai Xu-chu, Xu Pei-xia. A Novel Denoising Algorithm for Contaminated Chaotic Signals[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1849-1852. doi: 10.3724/SP.J.1146.2007.00043
Liu Kai, Li Hui, Dai Xu-chu, Xu Pei-xia. A Novel Denoising Algorithm for Contaminated Chaotic Signals[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1849-1852. doi: 10.3724/SP.J.1146.2007.00043
Citation:
Liu Kai, Li Hui, Dai Xu-chu, Xu Pei-xia. A Novel Denoising Algorithm for Contaminated Chaotic Signals[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1849-1852. doi: 10.3724/SP.J.1146.2007.00043
A novel algorithm for denoising the contaminated chaotic signals is proposed, which is based on Particle Filtering (PF), and adapted for low SNR, additive non-Gaussian noise and the chaotic dynamic system with unknown parameters. Basic idea behind the proposed algorithm is that, chaotic signal and unknown parameters in the chaotic dynamic system are considered as a high dimension state vector, and the joint posterior probability density of these state vectors can be recursively calculated by utilizing the principle of Particle Filtering, then the optimum estimation of chaotic signal can be attained. In order to overcome the degenerate phenomena caused by the rapid divergence of the chaotic orbits, an effective strategy is taken in the proposed algorithm. Kernel smoothing method and Auto Regression (AR) model are used to recursively estimate the non-time-varying and time-varying parameters, respectively. The simulation results show that, compared with the existing denoising methods, the proposed algorithm can more effectively denoise additive noise in contaminated chaotic signals.
Zhao Geng and Fang Jin-qing. Classification of chaos-basedcommunication and newest advances in chaotic securetechnique research. Chinese Journal of Nature, 2003, 25(1):21-30.[2]Kocarev L, Szczepanski J, and Amigo J M. Discrete chaos-I:theory[J].IEEE Trans. on Circuits and Systems I: FundamentalTheory and Applications.2006, 53(6):1300-[3]Brocker J, Parlitz U, and Ogorzalek M. Nonlinear noisereduction[J].Proc. IEEE.2002, 90(5):898-918[4]Yuan Jian and Xiao Xian-ci. Extracting the largest lyapunovexponents from the chaotic signals overwhelmed in the noise.Acta Electrnica Sinica, 1997, 25(10): 102-106.[5]Walker D M and Mees A I. Reconstructing nonlineardynamics by extended Kalman filtering[J].Int. J. Bifur. ChaosAppl. Sci. Eng.1998, 8(3):557-570[6]Feng Jiu-chao and Xie Sheng-li. An unscented transformbased filtering algorithm for noisy contaminated chaoticsignals. ISCAS 2006, Kos, May 2006: 2245-2248.[7]Doucet A, Freitas N D, and Gordon N. Sequential MonteCarlo Methods in Practice. New York: Springer, 2001:202-206.[8]Arulampalam M S, Maskell S, and Gordon N. A tutorial onparticle filters for online nonlinear /non-Gaussian Bayesiantracking[J].IEEE Trans. on Signal Processing.2002, 50(2):174-188
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