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A New Method for Separating EMI Signal Based on CEEMDAN and ICA

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Abstract

Aiming at the problem of electromagnetic interference signal separation, we propose a single-channel blind source separation method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA). Firstly, decompose the mixed interference signal by CEEMDAN to obtain a series of intrinsic mode functions (IMF). Secondly, combine them into a new multi-dimensional signal and solve its correlation matrix, and use singular value decomposition to obtain the eigenvalues of the matrix, and then use it to estimate the source number. Thirdly, select those IMFs whose correlation coefficients with the observed signal are bigger and regard them together with the original signal after denoising as new observed signals. Finally, recover the source signals by fast independent component analysis (Fast-ICA). We also extend our method to multi-channel BBS, and experiment results show that our method can eliminate unnecessary noise in the signal and perform well.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61771001).

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Authors and Affiliations

  1. LMIB, School of Mathematical Sciences, Beihang University, Beijing, 100191, China

    Di Zhao & Hongyi Li

  2. School of Software, Beihang University, Beijing, 100191, China

    Kunchang Li

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  1. Di Zhao
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  2. Kunchang Li
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  3. Hongyi Li
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Correspondence to Hongyi Li.

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Zhao, D., Li, K. & Li, H. A New Method for Separating EMI Signal Based on CEEMDAN and ICA. Neural Process Lett 53, 2243–2259 (2021). https://doi.org/10.1007/s11063-021-10432-x

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