Deep‐learning‐based line enhancer for passive sonar systems

作者：Donghao Ju, Cheng Chi, Zigao Li, Yu Li, Chunhua Zhang, Haining Huang · 发表于：IET Radar Sonar & Navigation · 年份：2021 · DOI：10.1049/rsn2.12205 · 被引用次数：15 · 研究领域：Underwater Acoustics Research、Speech and Audio Processing、Advanced Adaptive Filtering Techniques

Abstract Detection of acoustic tonals from surfaces and underwater vehicles is important for passive sonar systems. Enhancements of the tonals are usually necessary in passive sonar prior to detections. Conventionally, passive sonars employ adaptive line enhancers (ALE) in order to realise enhancements of the tonals. However, ALEs have requirements on their input signal‐to‐noise ratios (SNR). When the SNR inputs are too low, the ALEs cannot perform well. Therefore, for the purpose of overcoming the limitations of the SNR inputs to ALEs, this study proposes to enhance tonals using unsupervised deep‐learning techniques. The proposed deep‐learning‐based line enhancer (DLE) is based on an autoencoder neural network. The simulation results show that when the input SNR is −28 dB, the proposed DLE still achieves an SNR gain of 15 dB. However, the reference ALE fails. The experimental results also demonstrate the superiority of the proposed DLE.