A Novel Denoising Algorithm for Contaminated Chaotic Signals

Liu, Kai; Li, Hui; Dai, Xin; Pei-xia, XU

doi:10.3724/sp.j.1146.2007.00043

Cited by 3 publications

(1 citation statement)

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“…The recursive expressions of chaotic signals are used as state equations, the adaptive filter, such as unscented Kalman filtering and cubature Kalman filter, is adopted to estimate the signal. [5][6][7] These methods can achieve online denoising, but the system model and parameters need to be known as a prior. The second is that the system model is known but the parameters are unknown.…”

Section: Introductionmentioning

confidence: 99%

Chaotic signal denoising algorithm based on sparse decomposition*

Lv¹,

Zhang²,

Yuan³

2020

Chinese Phys. B

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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.

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Section: Introductionmentioning

confidence: 99%