报告详情
Transformer-Based Adaptive Line Enhancer for Passive Sonar Detection
编号：43
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更新：2024-10-23 10:49:40
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口头报告
报告开始：2024年11月02日 09:10（Asia/Shanghai）
报告时间：20min
所在会场：[P1] Parallel Session 1 [P1-2] Parallel Session 1（November 2 AM）
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摘要
The low-frequency narrow-band tonal components in the radiated noise of underwater targets are crucial features for passive sonar detection. Traditional adaptive line enhancer (ALE) exhibit limited performance at low signal-to-noise ratios (SNR). This paper proposes a Transformer-based adaptive line enhancer (TALE) to address this limitation. The proposed method leverages Transformer networks to enhance radiated noise signals from hydroacoustic targets in the time domain. The attention mechanism of the Transformer neural network enables the model to effectively learn both time-domain signal information and signal correlations. Simulation results demonstrate that the TALE algorithm offers significant spectral enhancement. Compared to traditional ALE and a deep-learning-based line enhancer (DLE), this algorithm can effectively improve the SNR of ship-radiated noise signals by 14 dB and 11 dB, respectively, under very low SNR conditions of -30 dB.
关键词
ship radiated noise,adaptive line enhancer,low signal-to-noise ratio (SNR),Transformer
报告人
OrdoqinHasqimeg
Mrs. Northwestern Polytechnical University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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