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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9319422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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