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Determination Method of Optimal Decomposition Level of Discrete Wavelet Based on Joint Jarque-Bera Test and
Zhanpeng Zhang1,2, Changjian Liu1,2, Min Wang1,2
1Key Laboratory of Smart Earth, Beijing 100080, China.
Entropy (Basel, Switzerland)
|February 26, 2025
Summary
This study introduces an adaptive wavelet denoising method using the Jarque-Bera test and composite weighting. It accurately determines the optimal decomposition level, enhancing signal smoothness and stability.
Area of Science:
- Signal Processing
- Data Analysis
- Wavelet Theory
Background:
- Traditional wavelet decomposition level selection methods have limitations.
- Existing evaluation indicators may not fully capture denoising performance.
- Accurate selection of decomposition levels is crucial for effective signal denoising.
Purpose of the Study:
- To propose an adaptive method for selecting the optimal wavelet decomposition level.
- To improve the accuracy and applicability of wavelet denoising.
- To overcome limitations of traditional evaluation indicators.
Main Methods:
- Utilized the Jarque-Bera test for noise characteristic validation.
- Selected RMSE and smoothness as key denoising evaluation metrics.
- Developed a composite weighting strategy combining entropy and coefficient of variation methods.
Main Results:
- The proposed method accurately determines the optimal wavelet decomposition level in various scenarios.
- Simulation results show high accuracy and good applicability.
- Experimental results demonstrate improved signal smoothness, waveform stability, and overall denoising performance.
Conclusions:
- The adaptive method effectively addresses limitations in selecting wavelet decomposition levels.
- The composite weighting approach enhances the reliability of evaluation metrics.
- This method offers a significant improvement in wavelet denoising applications.
Keywords:
Gaussian white noiseJarque–Bera testcombined weightingcomprehensive evaluation indicatoroptimal decomposition levelwavelet denoisingMore Related Videos
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