Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach

TL;DR

This paper introduces a deep learning-based scheme that enhances LOFAR spectrum analysis to improve underwater target recognition accuracy despite challenging noise conditions.

Contribution

It proposes a novel LOFAR spectrum enhancement method combined with CNN for underwater target recognition, significantly improving accuracy over existing methods.

Findings

Recognition accuracy of 95.22% achieved

Enhanced LOFAR spectrum improves feature quality

Outperforms state-of-the-art recognition methods

Abstract

The Low frequency analysis and recording (LOFAR) spectrum is one of the key features of the under water target, which can be used for underwater target recognition. However, the underwater environment noise is complicated and the signal-to-noise ratio of the underwater target is rather low, which introduces the breakpoints to the LOFAR spectrum and thus hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing. In preprocessing, a LOFAR spectrum enhancement based on multi-step decision algorithm is specifically designed to recover the breakpoints in LOFAR spectrum. In offline training, we then adopt the enhanced LOFAR spectrum as the input of convolutional neural network (CNN) and develop a LOFAR-based CNN (LOFAR-CNN) for online recognition. Taking advantage of the powerful capability of CNN in feature extraction, the proposed LOFAR-CNN can further improve the recognition accuracy. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of $95.22\%$, which outperforms the state-of-the-art methods.