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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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. ?

Název v anglickém jazyce

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

Popis výsledku anglicky

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. ?

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Svazek periodika

25

Číslo periodika v rámci svazku

17

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

20

Strana od-do

"nestrankovano"

Kód UT WoS článku

001570087500001

EID výsledku v databázi Scopus

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