TFLM: A time-frequency domain learning model for underwater acoustic signal reconstruction

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Ming Xu , Linjiang Liu
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引用次数: 0

Abstract

The reconstruction of underwater acoustic signals affected by seasonal variations is of great significance to improve the accuracy and stability of oceanic communication systems. Seasonal changes in water temperature, salinity, and density cause severe fluctuations and distortions to the underwater acoustic signals, which degrade the signal transmission quality. Existing reconstruction methods are often inadequate in adapting to these complex and dynamic environments and remain susceptible to noise interference. To tackle these challenges, we propose a novel Time-Frequency domain Learning Model (TFLM) for underwater acoustic signal reconstruction. TFLM decomposes the distorted signal into trend and seasonal components for reconstruction. The trend components are reconstructed using an enhanced Long Short-Term Memory (En- LSTM) that effectively captures long-term temporal features. For the seasonal components, a multi-layer encoder–decoder architecture is utilized to extract local features and address seasonal fluctuations. Extensive experimental evaluations demonstrate that TFLM outperforms existing methods in terms of reconstruction accuracy, providing a robust solution for underwater acoustic signal reconstruction under seasonal variability.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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