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Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning
Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the f...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434029/ https://www.ncbi.nlm.nih.gov/pubmed/34502716 http://dx.doi.org/10.3390/s21175827 |
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author | Shin, Myoungin Hong, Wooyoung Lee, Keunhwa Choo, Youngmin |
author_facet | Shin, Myoungin Hong, Wooyoung Lee, Keunhwa Choo, Youngmin |
author_sort | Shin, Myoungin |
collection | PubMed |
description | Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components. |
format | Online Article Text |
id | pubmed-8434029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84340292021-09-12 Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning Shin, Myoungin Hong, Wooyoung Lee, Keunhwa Choo, Youngmin Sensors (Basel) Article Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components. MDPI 2021-08-30 /pmc/articles/PMC8434029/ /pubmed/34502716 http://dx.doi.org/10.3390/s21175827 Text en © 2021 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 Shin, Myoungin Hong, Wooyoung Lee, Keunhwa Choo, Youngmin Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning |
title | Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning |
title_full | Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning |
title_fullStr | Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning |
title_full_unstemmed | Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning |
title_short | Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning |
title_sort | frequency analysis of acoustic data using multiple-measurement sparse bayesian learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434029/ https://www.ncbi.nlm.nih.gov/pubmed/34502716 http://dx.doi.org/10.3390/s21175827 |
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