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Related Experiment Video

Updated: Jun 11, 2026

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High-SNR phase unwrap method based on RMN-LSTM for distributed hydro-acoustic detection.

Yage Zhan, Weigang Deng, Zhaoyong Wang

    Applied Optics
    |June 10, 2026
    PubMed
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    A novel RMN-LSTM neural network enhances underwater target detection by accurately reconstructing ship acoustic fingerprints from noisy data. This method significantly improves signal quality without requiring hardware upgrades.

    Area of Science:

    • Marine acoustics
    • Signal processing
    • Artificial intelligence

    Background:

    • Distributed acoustic sensing (DAS) is promising for underwater target detection.
    • Ship detection relies on line-spectrum components, which are vulnerable to marine noise.
    • Noise degrades the signal-to-noise ratio (SNR), causing phase distortions in DAS data.

    Purpose of the Study:

    • To develop a robust method for high-SNR phase unwrap and line-spectrum target detection.
    • To address challenges posed by complex marine ambient noises in DAS.
    • To improve the accuracy of underwater acoustic target identification.

    Main Methods:

    • Proposed a residual module nested long short-term memory (RMN-LSTM) neural network.
    • Combined deep spatial feature extraction with long-range temporal dependency.

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  • Employed a symmetric left-right encoder-decoder network structure.
  • Learned direct mapping between distorted wrapped and true phase signals.
  • Main Results:

    • Achieved accurate reconstruction of line-spectrum signals.
    • Significantly outperformed conventional algorithms and CNN-based methods.
    • Reduced root mean square error (RMSE) to 0.661.
    • Obtained a comprehensive SNR improvement of approximately 27 dB.

    Conclusions:

    • The RMN-LSTM method effectively recovers high-fidelity phase information from noisy DAS data.
    • This approach provides a robust technical solution for high-SNR underwater target detection.
    • No hardware upgrades are necessary, making the method broadly applicable.