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An Adaptive Denoising Algorithm for Chaotic Signals Based on Improved Empirical Mode Decomposition

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Abstract

It is difficult to determine the threshold of mode cell in the interval-thresholding algorithm, when it is used to denoise chaotic signals. In this paper, an adaptive denoising algorithm is proposed for chaotic signals based on improved empirical mode decomposition. First, the noisy chaotic signal is decomposed into the intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition. Then, the zero-crossing scale thresholding denoising algorithm is used to denoise the IMFs with different thresholds. The optimal threshold is obtained by the Durbin–Watson criterion. With the optimal threshold, the final denoised chaotic signal is obtained. The proposed algorithm effectively solves the issue mentioned above. The experimental results show the proposed algorithm can denoise noisy chaotic signals in different conditions effectively and is better than other existing algorithms.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61471310, 11747087), the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 17C1530), and the Natural Science Foundation of Xiangtan University, China (Grant No. 15XZX33).

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Authors and Affiliations

  1. College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Mengjiao Wang, Zequan Zhou & Zhijun Li

  2. School of Physics and Optoelectric Engineering, Xiangtan University, Xiangtan, 411105, China

    Yicheng Zeng

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  1. Mengjiao Wang
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  2. Zequan Zhou
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  3. Zhijun Li
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  4. Yicheng Zeng
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Correspondence to Mengjiao Wang.

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Wang, M., Zhou, Z., Li, Z. et al. An Adaptive Denoising Algorithm for Chaotic Signals Based on Improved Empirical Mode Decomposition. Circuits Syst Signal Process 38, 2471–2488 (2019). https://doi.org/10.1007/s00034-018-0973-7

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