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Sensor-Oriented Framework for Underwater Acoustic Signal Classification Using EMD–Wavelet Filtering and Bayesian-Optimized Random Forest

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Vydáno v:Sensors vol. 25, no. 17 (2025), p. 5336-5356
Hlavní autor: Babichev Sergii
Další autoři: Yarema Oleg, Khomenko Yevheniy, Senchyshen Denys, Durnyak Bohdan
Vydáno:
MDPI AG
Témata:
Machine learning
Accuracy
Datasets
Deep learning
Maritime industry
Fourier transforms
Signal to noise ratio
Optimization techniques
Real time
Neural networks
Signal processing
Classification
Algorithms
Automation
Acoustics
Statistical methods
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Abstrakt:Ship acoustic signal classification is essential for vessel identification, underwater navigation, and maritime security. Traditional methods struggle with the non-stationary nature and noise of ship acoustic signals, reducing classification accuracy. To address these challenges, we propose an automated pipeline that integrates Empirical Mode Decomposition (EMD), adaptive wavelet filtering, feature selection, and a Bayesian-optimized Random Forest classifier. The framework begins with EMD-based decomposition, where the most informative Intrinsic Mode Functions (IMFs) are selected using Signal-to-Noise Ratio (SNR) analysis. Wavelet filtering is applied to reduce noise, with optimal wavelet parameters determined via SNR and Stein’s Unbiased Risk Estimate (SURE) criteria. Features extracted from statistical, frequency domain (FFT), and time–frequency (wavelet) metrics are ranked, and the top 11 most important features are selected for classification. A Bayesian-optimized Random Forest classifier is trained using the extracted features, ensuring optimal hyperparameter selection and reducing computational complexity. The classification results are further enhanced using a majority voting strategy, improving the accuracy of the final object identification. The proposed approach demonstrates high accuracy, improved noise suppression, and robust classification performance. The methodology is scalable, computationally efficient, and suitable for real-time maritime applications.
ISSN:1424-8220
DOI:10.3390/s25175336
Zdroj:Health & Medical Collection