Chaotic signal denoising algorithm based on sparse decomposition

doi:10.1088/1674-1056/ab8a3b

Abstract Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics. The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics. We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition (K-SVD) optimization. The observed signal is divided into segments and decomposed sparsely. The over-complete atomic library is constructed according to the differential equation of chaotic signals. The orthogonal matching pursuit algorithm is used to search the optimal matching atom. The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD. The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.

Keywords: sparse decomposition denoising K-SVD chaotic signal

Received: 27 February 2020
Revised: 10 April 2020
Accepted manuscript online:

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	05.40.Ca	(Noise)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61872083) and the Natural Science Foundation of Guangdong Province, China (Grant Nos. 2017A030310659 and 2019A1515011123).

Corresponding Authors: Hua-Qiang Yuan
E-mail: yuanhq@dgut.edu.cn

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Jin-Wang Huang(黄锦旺), Shan-Xiang Lv(吕善翔), Zu-Sheng Zhang(张足生), Hua-Qiang Yuan(袁华强) Chaotic signal denoising algorithm based on sparse decomposition 2020 Chin. Phys. B 29 060505

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