Self-tuning Multivariate Variational Mode Decomposition

LANG Xun; WANG Jiayi; CHEN Qiming; HE Bingbing; MAO Rukai; XIE Lei

doi:10.11999/JEIT230763

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2994-3001

LANG Xun, WANG Jiayi, CHEN Qiming, HE Bingbing, MAO Rukai, XIE Lei. Self-tuning Multivariate Variational Mode Decomposition[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2994-3001. doi: 10.11999/JEIT230763

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LANG Xun, WANG Jiayi, CHEN Qiming, HE Bingbing, MAO Rukai, XIE Lei. Self-tuning Multivariate Variational Mode Decomposition[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2994-3001. doi: 10.11999/JEIT230763

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Self-tuning Multivariate Variational Mode Decomposition

doi: 10.11999/JEIT230763

1.
School of Information Science and Engineering, Yunnan University, Kunming 650504, China
2.
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
3.
Yunnan Yuntianhua Limited by Share Ltd., Kunming 650000, China

Funds: The National Natural Science Foundation of China (62003298, 62201495), Yunnan Fundamental Research Projects (202301AT070277), The Major Project of Science and Technology of Yunnan Province (202202AD080005, 202202AH080009)

Received Date: 2023-07-25
Rev Recd Date: 2024-04-27

Available Online: 2024-05-15

Publish Date: 2024-07-29

Abstract

Abstract

The Multivariate Variational Mode Decomposition (MVMD), being an extension of the Variational Mode Decomposition (VMD), inherits the merits of VMD. However, it encounters an issue wherein its decomposition performance relies heavily on two predefined parameters, the number of modes (K) and the penalty factor ($ \alpha $). To address this issue, a Self-tuning MVMD (SMVMD) algorithm is proposed. SMVMD employs the notion of matching pursuit to adaptively update K and $ \alpha $ based on energy occupation and mode orthogonality in the frequency domain, respectively. The experimented results of both simulated signals and real cases demonstrate that the proposed SMVMD not only effectively addresses the parameter rectification problem of the original MVMD, but also exhibits the following advantages: (1) SMVMD displays superior resilience to mode-mixing compared to MVMD, along with enhanced robustness to both noise and variations in $ \alpha $-value. (2) In comparison to the classical algorithms of multivariate empirical mode decomposition, fast multivariate empirical mode decomposition, and multivariate variational mode decomposition, SMVMD showcases the lowest decomposition error and the best decomposition effect.
- Multivariate signal processing,
- Multivariate Variational Mode Decomposition (MVMD),
- Self-tuning,
- Matching pursuit method,
- Robustness

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References(28)

References

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