Extracting Chaotic Signal from Noisy Environment: A Random Searching Method

LI Guangming; LYU Shanxiang

doi:10.1049/cje.2015.07.025

Volume 24 Issue 3

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LI Guangming and LYU Shanxiang, “Extracting Chaotic Signal from Noisy Environment: A Random Searching Method,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 584-589, 2015, doi: 10.1049/cje.2015.07.025

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LI Guangming and LYU Shanxiang, “Extracting Chaotic Signal from Noisy Environment: A Random Searching Method,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 584-589, 2015, doi: 10.1049/cje.2015.07.025

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Extracting Chaotic Signal from Noisy Environment: A Random Searching Method

doi: 10.1049/cje.2015.07.025

LI Guangming¹,
LYU Shanxiang^2,3

1.
School of Computer Science, Dongguan University of Technology, Dongguan 523808, China;
2.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China;
3.
Department of Electrical and Electronic Engineering, Imperial College London, London SW72AZ, United Kingdom

Funds: This work is supported by the National Natural Science Foundation of China (No.61170216, No.61372082).

Received Date: 2014-08-12
Rev Recd Date: 2014-10-15
Publish Date: 2015-07-10

Abstract

Abstract

The problem of Blind source extraction (BSE) regarding a chaotic signal is addressed in this paper, by employing the newly defined Proliferation exponent (PE). The properties of gradient pursuit algorithm based on PE are further articulated, especially the infelicity to tackle a BSE problem. Subsequently, we devise a constraint optimization algorithm called Orthogonal random searching (ORS) to accomplish the optimization task, which in essence searches for optimal solutions with the principle of Markov chain Monte Carlo (MCMC). Experimental results reveal that this PE-based random searching method can extract the desired chaotic signal among multiple Gaussian mixtures, as well as showing robustness against noise contamination.
- Blind source extraction,
- Chaotic signal,
- Markov chain Monte Carlo (MCMC)

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References(23)

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