Preprocessing LOFARgram through U-Net++ neural network
The study of the low-frequency analysis and recording spectrum (LOFARgram) of ship-radiated noise is essential for extracting critical information, such as target motion trajectories. However, the quality of LOFARgrams often degrades due to the inherent stochasticity of ship noise and the interferen...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1528111/full |
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| _version_ | 1832582252077252608 |
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| author | Dan Peng Xichen Xu Wenhua Song Dazhi Gao |
| author_facet | Dan Peng Xichen Xu Wenhua Song Dazhi Gao |
| author_sort | Dan Peng |
| collection | DOAJ |
| description | The study of the low-frequency analysis and recording spectrum (LOFARgram) of ship-radiated noise is essential for extracting critical information, such as target motion trajectories. However, the quality of LOFARgrams often degrades due to the inherent stochasticity of ship noise and the interference of environmental noise. We significantly enhance the clarity and quality of LOFARgrams by employing the U-Net++ neural network model for preprocessing. Effective training of neural network models usually requires large datasets, but the available actual LOFARgrams are often limited and costly to collect. To ensure an adequate dataset for neural network training, this paper introduces an innovative forward model that simulates LOFARgrams from stochastic noise sources. This model uses explosive decaying cosine pulses as basic units to simulate ship noise sources and employs the KRAKEN normal mode model to simulate the underwater acoustic channel’s transfer function, thereby efficiently creating high-fidelity ship noise LOFARgrams. The forward model supplies sufficient data to train the U-Net++ neural network, enabling it to demonstrate effective recovery of LOFARgrams. Additionally, we introduce a new algorithm that utilizes data prior to the Closest Point of Approach (CPA) to predict the CPA parameters, applied to both the original LOFARgrams and those processed with U-Net++. Results indicate that predictions based on U-Net++ enhanced LOFARgrams are more accurate. Our work demonstrate the effectiveness of the forward model and U-Net++ enhanced LOFARgrams for ship-radiated noise analysis and precise prediction of target motion. |
| format | Article |
| id | doaj-art-a5d6057bc3f942da8ae4cb118d8bf9af |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-a5d6057bc3f942da8ae4cb118d8bf9af2025-01-30T05:10:33ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011210.3389/fmars.2025.15281111528111Preprocessing LOFARgram through U-Net++ neural networkDan Peng0Xichen Xu1Wenhua Song2Dazhi Gao3College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, ChinaCollege of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, ChinaCollege of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, ChinaCollege of Marine Technology, Ocean University of China, Qingdao, ChinaThe study of the low-frequency analysis and recording spectrum (LOFARgram) of ship-radiated noise is essential for extracting critical information, such as target motion trajectories. However, the quality of LOFARgrams often degrades due to the inherent stochasticity of ship noise and the interference of environmental noise. We significantly enhance the clarity and quality of LOFARgrams by employing the U-Net++ neural network model for preprocessing. Effective training of neural network models usually requires large datasets, but the available actual LOFARgrams are often limited and costly to collect. To ensure an adequate dataset for neural network training, this paper introduces an innovative forward model that simulates LOFARgrams from stochastic noise sources. This model uses explosive decaying cosine pulses as basic units to simulate ship noise sources and employs the KRAKEN normal mode model to simulate the underwater acoustic channel’s transfer function, thereby efficiently creating high-fidelity ship noise LOFARgrams. The forward model supplies sufficient data to train the U-Net++ neural network, enabling it to demonstrate effective recovery of LOFARgrams. Additionally, we introduce a new algorithm that utilizes data prior to the Closest Point of Approach (CPA) to predict the CPA parameters, applied to both the original LOFARgrams and those processed with U-Net++. Results indicate that predictions based on U-Net++ enhanced LOFARgrams are more accurate. Our work demonstrate the effectiveness of the forward model and U-Net++ enhanced LOFARgrams for ship-radiated noise analysis and precise prediction of target motion.https://www.frontiersin.org/articles/10.3389/fmars.2025.1528111/fullship noise modelsimulate ship LOFARgramU-Net++ neural networkLOFARgram preprocesstarget motion analysis |
| spellingShingle | Dan Peng Xichen Xu Wenhua Song Dazhi Gao Preprocessing LOFARgram through U-Net++ neural network Frontiers in Marine Science ship noise model simulate ship LOFARgram U-Net++ neural network LOFARgram preprocess target motion analysis |
| title | Preprocessing LOFARgram through U-Net++ neural network |
| title_full | Preprocessing LOFARgram through U-Net++ neural network |
| title_fullStr | Preprocessing LOFARgram through U-Net++ neural network |
| title_full_unstemmed | Preprocessing LOFARgram through U-Net++ neural network |
| title_short | Preprocessing LOFARgram through U-Net++ neural network |
| title_sort | preprocessing lofargram through u net neural network |
| topic | ship noise model simulate ship LOFARgram U-Net++ neural network LOFARgram preprocess target motion analysis |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1528111/full |
| work_keys_str_mv | AT danpeng preprocessinglofargramthroughunetneuralnetwork AT xichenxu preprocessinglofargramthroughunetneuralnetwork AT wenhuasong preprocessinglofargramthroughunetneuralnetwork AT dazhigao preprocessinglofargramthroughunetneuralnetwork |