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Comparative Study on Feature Extraction of Marine Background Noise Based on Nonlinear Dynamic Features.
Guanni Ji1, Yu Wang1, Fei Wang1
1School of Zhongxing Communication, Xi'an Traffic Engineering Institute, Xi'an 710300, China.
Entropy (Basel, Switzerland)
|June 28, 2023
Summary
Extracting marine background noise (MBN) features is challenging. This study shows nonlinear dynamics features, including entropy and Lempel-Ziv complexity (LZC), effectively capture MBN complexity for improved environmental parameter inversion.
Area of Science:
- Oceanography
- Signal Processing
- Complexity Science
Background:
- Marine background noise (MBN) is crucial for understanding marine environments.
- Extracting MBN features is complex due to the marine environment's intricacies.
Purpose of the Study:
- To investigate nonlinear dynamics features for effective MBN feature extraction.
- To compare the performance of various entropy and Lempel-Ziv complexity (LZC) based methods.
Main Methods:
- Comparative analysis of entropy-based methods: dispersion entropy (DE), permutation entropy (PE), fuzzy entropy (FE), and sample entropy (SE).
- Comparative analysis of Lempel-Ziv complexity (LZC)-based methods: LZC, dispersion LZC (DLZC), permutation LZC (PLZC), and dispersion entropy-based LZC (DELZC).
- Evaluation using simulation experiments to assess time series complexity detection and actual MBN data for feature extraction performance.
Main Results:
- All tested nonlinear dynamics features effectively detect changes in time series complexity.
- Both entropy-based and LZC-based feature extraction methods demonstrate superior performance for MBN.
Conclusions:
- Nonlinear dynamics features offer a robust approach for MBN feature extraction.
- The developed methods enhance the potential for accurate marine environmental parameter inversion using MBN.
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