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Method to Solve Underwater Laser Weak Waves and Superimposed Waves
With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and sup...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346930/ https://www.ncbi.nlm.nih.gov/pubmed/37447907 http://dx.doi.org/10.3390/s23136058 |
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author | Kang, Chuanli Lin, Zitao Wu, Siyi Yang, Jiale Zhang, Siyao Zhang, Sai Li, Xuanhao |
author_facet | Kang, Chuanli Lin, Zitao Wu, Siyi Yang, Jiale Zhang, Siyao Zhang, Sai Li, Xuanhao |
author_sort | Kang, Chuanli |
collection | PubMed |
description | With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R(2) is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg–Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R(2) is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves. |
format | Online Article Text |
id | pubmed-10346930 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103469302023-07-15 Method to Solve Underwater Laser Weak Waves and Superimposed Waves Kang, Chuanli Lin, Zitao Wu, Siyi Yang, Jiale Zhang, Siyao Zhang, Sai Li, Xuanhao Sensors (Basel) Article With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R(2) is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg–Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R(2) is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves. MDPI 2023-06-30 /pmc/articles/PMC10346930/ /pubmed/37447907 http://dx.doi.org/10.3390/s23136058 Text en © 2023 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 Kang, Chuanli Lin, Zitao Wu, Siyi Yang, Jiale Zhang, Siyao Zhang, Sai Li, Xuanhao Method to Solve Underwater Laser Weak Waves and Superimposed Waves |
title | Method to Solve Underwater Laser Weak Waves and Superimposed Waves |
title_full | Method to Solve Underwater Laser Weak Waves and Superimposed Waves |
title_fullStr | Method to Solve Underwater Laser Weak Waves and Superimposed Waves |
title_full_unstemmed | Method to Solve Underwater Laser Weak Waves and Superimposed Waves |
title_short | Method to Solve Underwater Laser Weak Waves and Superimposed Waves |
title_sort | method to solve underwater laser weak waves and superimposed waves |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346930/ https://www.ncbi.nlm.nih.gov/pubmed/37447907 http://dx.doi.org/10.3390/s23136058 |
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