Chaotic Signal De-noising Based on Adaptive Threshold Synchrosqueezed Wavelet Transform

doi:10.3969/j.issn.0372-2112.2018.07.016

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ACTA ELECTRONICA SINICA ›› 2018, Vol. 46 ›› Issue (7) : 1652-1657. DOI: 10.3969/j.issn.0372-2112.2018.07.016

Chaotic Signal De-noising Based on Adaptive Threshold Synchrosqueezed Wavelet Transform

WANG Wen-bo¹, JIN Yun-yu¹, WANG Bin², LI Wei-gang², WANG Xiang-li³

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Abstract

For the lack of the single threshold denoising method of synchrosqueezed wavelet transform(SST),an improved denoising method for chaotic signal is proposed based on SST hierarchical threshold.Firstly,according to the distribution models of SST decomposition coefficients of the signal and the noise,the formula of mean square error of SST chaotic signal denoising is derived,which contains the threshold coefficients of amplitude.Then,the optimal threshold coefficients of amplitude is calculated based on the minimum mean square error criterion.Finally,the optimal hierarchical thresholds of SST chaotic denoising is determined according to the optimal threshold coefficients and the standard deviation of the noise.In the experiments,the denoising performance of the proposed method is tested by using the simulated chaotic signals and the measured monthly sunspot signals.The experimental results show that the proposed method can filter the noise of chaotic signal better,and the chaotic properties of the originals can be largely recovered.The proposed method can obtain better performance in the chaotic signal denoising than the classical wavelet transform threshold method and the EEMD denoising method.

Key words

synchrosqueezed wavelet transform / de-noising / chaotic signal / hierarchical threshold

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WANG Wen-bo, JIN Yun-yu, WANG Bin, LI Wei-gang, WANG Xiang-li. Chaotic Signal De-noising Based on Adaptive Threshold Synchrosqueezed Wavelet Transform[J]. Acta Electronica Sinica, 2018, 46(7): 1652-1657. https://doi.org/10.3969/j.issn.0372-2112.2018.07.016

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Funding

National Natural Science Foundation of China (No.61671338, No.61473213, No.51774219); Fund of Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (No.znxx2018QN04, No.znxx2018QN01)