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Adaptive Hoyer-L-Moment Envelope Spectrum: A Method for Robust Demodulation of Ship-Radiated Noise in Low-SNR Environments

  • 0Science and Technology on Sonar Laboratory, Hangzhou 310023, China.
Sensors (basel, Switzerland) +

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December 31, 2025

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December 31, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an Adaptive Hoyer-L-moment Envelope Spectrum (AHLES) method to improve ship propeller noise analysis. AHLES enhances the extraction of crucial modulation features, aiding in vessel target identification, especially in noisy conditions.

Area Of Science

  • Underwater acoustics
  • Signal processing
  • Naval engineering

Background

  • Propeller noise is a primary source of ship-radiated noise, critical for vessel identification.
  • Current demodulation techniques struggle with low signal-to-noise ratios (SNR) due to interference.
  • Optimal frequency band selection is key to improving demodulation performance.

Purpose Of The Study

  • To propose a novel Adaptive Hoyer-L-moment Envelope Spectrum (AHLES) method for enhanced propeller noise analysis.
  • To improve the extraction of modulation characteristics from ship-radiated noise.
  • To enhance vessel target classification and identification capabilities.

Main Methods

  • Introduced an optimal frequency band selection using a golden section search strategy.
  • Designed a Hoyer-L-moment metric to quantify modulation intensity in narrow frequency bands.
  • Adaptively selected spectral coherence integration bands based on signal modulation characteristics.

Main Results

  • The proposed AHLES method effectively extracts modulation features even in low SNR environments.
  • Demonstrated improved demodulation performance compared to existing methods.
  • Validated through simulations and real-world merchant ship data.

Conclusions

  • The AHLES method offers a robust approach for analyzing ship propeller noise.
  • It significantly enhances the ability to identify vessel targets by improving modulation feature extraction.
  • This technique is valuable for underwater acoustic surveillance and target recognition.

Keywords:

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