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Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more r...

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Detalles Bibliográficos
Autores principales: You, Heewon, Byun, Sung-Hoon, Choo, Youngmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319422/
https://www.ncbi.nlm.nih.gov/pubmed/35890767
http://dx.doi.org/10.3390/s22145088
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author You, Heewon
Byun, Sung-Hoon
Choo, Youngmin
author_facet You, Heewon
Byun, Sung-Hoon
Choo, Youngmin
author_sort You, Heewon
collection PubMed
description It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more reliable detection results using abundant training data, costing intensive time and labor. We propose a method with favorable detection performance by using a hidden Markov model (HMM) for sequential acoustic data, which requires no separate training data. Since the detection results from HMM are significantly affected by the random initial parameters of HMM, the genetic algorithm (GA) is adopted to reduce the sensitivity of the initial parameters. The tuned initial parameters from GA are used as a start point for the subsequent Baum–Welch algorithm updating the HMM parameters. Furthermore, multiple measurements from arrays are exploited both in determining the proper initial parameters with GA and updating the parameters with the Baum–Welch algorithm. In contrast to the standard random selection of the initial point with single measurement, a stable initial point setting by the GA ensures improved SOI detections with the Baum–Welch algorithm using the multiple measurements, which are demonstrated in passive and active acoustic data. Particularly, the proposed method shows the most confidential detection in finding weak elastic surface waves from target, compared to existing methods such as conventional HMM.
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spelling pubmed-93194222022-07-27 Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements You, Heewon Byun, Sung-Hoon Choo, Youngmin Sensors (Basel) Article It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more reliable detection results using abundant training data, costing intensive time and labor. We propose a method with favorable detection performance by using a hidden Markov model (HMM) for sequential acoustic data, which requires no separate training data. Since the detection results from HMM are significantly affected by the random initial parameters of HMM, the genetic algorithm (GA) is adopted to reduce the sensitivity of the initial parameters. The tuned initial parameters from GA are used as a start point for the subsequent Baum–Welch algorithm updating the HMM parameters. Furthermore, multiple measurements from arrays are exploited both in determining the proper initial parameters with GA and updating the parameters with the Baum–Welch algorithm. In contrast to the standard random selection of the initial point with single measurement, a stable initial point setting by the GA ensures improved SOI detections with the Baum–Welch algorithm using the multiple measurements, which are demonstrated in passive and active acoustic data. Particularly, the proposed method shows the most confidential detection in finding weak elastic surface waves from target, compared to existing methods such as conventional HMM. MDPI 2022-07-06 /pmc/articles/PMC9319422/ /pubmed/35890767 http://dx.doi.org/10.3390/s22145088 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
You, Heewon
Byun, Sung-Hoon
Choo, Youngmin
Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements
title Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements
title_full Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements
title_fullStr Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements
title_full_unstemmed Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements
title_short Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements
title_sort underwater acoustic signal detection using calibrated hidden markov model with multiple measurements
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319422/
https://www.ncbi.nlm.nih.gov/pubmed/35890767
http://dx.doi.org/10.3390/s22145088
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