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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...

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Autores principales: Kang, Chuanli, Lin, Zitao, Wu, Siyi, Yang, Jiale, Zhang, Siyao, Zhang, Sai, Li, Xuanhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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