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Underwater Source Counting with Local-Confidence-Level-Enhanced Density Clustering.

Yang Chen1, Yuanzhi Xue1, Rui Wang1

  • 1School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced source counting method for underwater platforms using a single acoustic vector sensor. The technique improves accuracy by refining density clustering with local confidence levels.

Keywords:
DOA estimationacoustic vector sensordensity clusteringlocal confidence levelsource counting

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Area of Science:

  • Underwater acoustics
  • Signal processing
  • Autonomous systems

Background:

  • Accurate source counting is crucial for autonomous underwater platforms.
  • Existing methods may struggle with complex acoustic environments.

Purpose of the Study:

  • To propose an improved source counting method for single acoustic vector sensors.
  • To enhance the accuracy of autonomous underwater platform detection.

Main Methods:

  • Calculating short-time Fourier transforms (STFT) of sound pressure and vibration velocity.
  • Estimating directions of arrival (DOAs) from time-frequency data.
  • Applying local-confidence-level-enhanced density clustering to classify DOAs.

Main Results:

  • The enhanced method successfully classifies DOAs and determines source numbers.
  • Local confidence weighting improved density differences between cluster centers and sidelobes.
  • Achieved superior source counting accuracy compared to basic density clustering.

Conclusions:

  • The proposed local-confidence-level-enhanced density clustering method significantly improves source counting accuracy.
  • This method offers a more robust solution for source counting in autonomous underwater applications.
  • The technique leverages single acoustic vector sensor data effectively.