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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F25%3A43899247" target="_blank" >RIV/44555601:13440/25:43899247 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/25/17/5336" target="_blank" >https://www.mdpi.com/1424-8220/25/17/5336</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s25175336" target="_blank" >10.3390/s25175336</a>

Result language

angličtina

Original language name

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

Original language description

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. ?

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

25

Issue of the periodical within the volume

17

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

"nestrankovano"

UT code for WoS article

001570087500001

EID of the result in the Scopus database

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