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Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements.

Heewon You1, Sung-Hoon Byun2,3, Youngmin Choo4

  • 1Department of Ocean Systems Engineering, Sejong University, Seoul 05006, Korea.

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Summary
This summary is machine-generated.

This study introduces a novel method using a hidden Markov model (HMM) optimized by a genetic algorithm (GA) for improved signal of interest (SOI) detection in sonar systems. The approach enhances detection accuracy, especially for weak signals, without needing extensive training data.

Keywords:
genetic algorithmhidden Markov modelsonar signal detection

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Traditional threshold-based methods for signal of interest (SOI) detection in sonar systems suffer from high false alarm rates, particularly at low signal-to-noise ratios.
  • Machine learning approaches offer improved reliability but require substantial time and labor for training data acquisition.
  • Existing methods often struggle with detecting weak signals, necessitating more robust detection techniques.

Purpose of the Study:

  • To develop a novel and reliable method for detecting signals of interest (SOIs) in acoustic data.
  • To enhance the performance of hidden Markov models (HMMs) in SOI detection by optimizing initial parameters.
  • To reduce the dependency on large, labeled training datasets for acoustic signal detection.

Main Methods:

  • A hidden Markov model (HMM) is employed for the sequential analysis of acoustic data.
  • A genetic algorithm (GA) is utilized to optimize the initial parameters of the HMM, mitigating sensitivity to random starting points.
  • The Baum-Welch algorithm is used to update HMM parameters, with GA-tuned initial parameters providing a stable starting point.
  • Multiple measurements from acoustic arrays are incorporated into both the GA parameter optimization and the Baum-Welch updating steps.

Main Results:

  • The proposed HMM-GA method demonstrates favorable detection performance for signals of interest (SOIs) in both passive and active acoustic data.
  • Optimization of initial HMM parameters using the genetic algorithm (GA) leads to more stable and reliable detection outcomes.
  • The method effectively detects weak elastic surface waves from targets, outperforming conventional HMM approaches.
  • Utilizing multiple measurements enhances the stability and accuracy of the parameter estimation and subsequent detection.

Conclusions:

  • The integrated genetic algorithm (GA) and hidden Markov model (HMM) approach provides a robust and efficient solution for signal of interest (SOI) detection in sonar.
  • This method overcomes the limitations of traditional techniques and data-intensive machine learning models by requiring no separate training data.
  • The GA-driven parameter optimization significantly improves the reliability of HMM-based acoustic detection, particularly for challenging weak signal scenarios.