Angle-distance decomposition based on deep learning for active sonar detection

TL;DR

This paper introduces a deep learning approach for active sonar target detection that decomposes the process into angle and distance estimation, improving robustness in complex underwater environments.

Contribution

It proposes a novel deep learning-based method that separates angle and distance estimation for active sonar detection, addressing data scarcity with transfer learning and simulation.

Findings

Effective target detection in noisy underwater environments

Robust performance demonstrated through experiments

Utilization of transfer learning and simulation enhances training

Abstract

Underwater target detection using active sonar constitutes a critical research area in marine sciences and engineering. However, traditional signal processing methods face significant challenges in complex underwater environments due to noise, reverberation, and interference. To address these issues, this paper presents a deep learning-based active sonar target detection method that decomposes the detection process into separate angle and distance estimation tasks. Active sonar target detection employs deep learning models to predict target distance and angle, with the final target position determined by integrating these estimates. Limited underwater acoustic data hinders effective model training, but transfer learning and simulation offer practical solutions to this challenge. Experimental results verify that the method achieves effective and robust performance under challenging conditions.