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A Sensing and Tracking Algorithm for Multiple Frequency Line Components in Underwater Acoustic Signals.

Xinwei Luo¹, Zihan Shen¹

¹Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)

|November 14, 2019

Summary

This paper introduces a novel Hidden Markov Model (HMM) method for detecting and tracking multiple frequency lines in underwater acoustic signals. The approach enhances sensing capabilities for weak, time-varying signals, even at low signal-to-noise ratios (SNR).

Keywords:

HMM frequency line detection lofargram lofargram segmentation

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Area of Science:

Signal Processing
Acoustics
Machine Learning

Background:

Underwater acoustic signal analysis often struggles with detecting and tracking multiple weak or time-varying frequency components.
Existing methods may face challenges in low signal-to-noise ratio (SNR) environments and with complex signal patterns.

Purpose of the Study:

To develop an automated method for reliable and efficient sensing and tracking of multiple frequency lines in underwater acoustic signals.
To improve the detection of weak and time-varying frequency components in lofargrams, particularly under low SNR conditions.

Main Methods:

A novel approach using Hidden Markov Models (HMM) for automatic detection and tracking of multiple frequency lines in lofargrams.
Segmentation of lofargrams into sub-lofargrams for targeted screening and HMM-based detection.
Image stitching and statistical modeling for merging frequency lines detected across different sub-lofargrams.

Main Results:

The proposed HMM-based method effectively detects multiple time-varying frequency lines in underwater acoustic signals.
The algorithm demonstrates robust performance even under low signal-to-noise ratio (SNR) conditions.
Significant reduction in computational load compared to traditional methods.

Conclusions:

The developed algorithm offers enhanced multiple frequency line sensing abilities for underwater acoustic signals.
The method provides a computationally efficient technique for feature sensing and tracking.
Potential applications include unattended equipment like sonar and submerged buoys.