Modulation Feature Enhancement with a Multi-Stage Attention Network for Underwater Acoustic Target Recognition

TL;DR

This paper presents a deep learning framework utilizing multi-stage attention mechanisms and spectral feature enhancement to improve underwater acoustic target recognition, especially under class imbalance conditions.

Contribution

It introduces a novel multi-stage attention network with specialized spectral attention modules and an adjustable loss function for better recognition accuracy.

Findings

Enhanced recognition accuracy on real-world ship noise data.

Effective handling of class imbalance with ACBFL.

Superior performance compared to existing methods.

Abstract

Underwater acoustic target recognition is critical for maritime applications, yet it faces challenges arising from the complex and diverse nature of ship-radiated noise. To address these issues, we propose a robust deep learning-based framework. First, we introduce a feature extraction and fusion method based on variational mode decomposition (VMD) and the 3/2-D spectrum to generate high-fidelity 2-D DEMON spectral features, which effectively capture modulation envelope information. To further enhance feature representation, we design a one-dimensional convolutional neural network (1-D CNN) integrated with a novel Multi-Stage Multi-Type Attention Mechanism (MMATT) that adaptively refines features at different network depths. Within this mechanism, we propose a Residual Channel-Independent Spectral Attention Mechanism (R-CISAM) and a Multi-Scale Separate-and-Fuse Spectral Attention Mechanism (MS-SFSAM). Moreover, to mitigate performance degradation caused by severe class imbalance inherent in real-world ship-radiated noise data, we devise an Adjustable Class-Balanced Focal Loss (ACBFL), which provides flexibility across tasks with varying degrees of imbalance. Experimental results on a real-world ship-radiated noise dataset demonstrate that the proposed solutions effectively enhance underwater acoustic target recognition performance.