Underwater acoustic multi-target recognition based on channel attention mechanism

Abstract

The recognition of ship-radiated noise presents a critical challenge in underwater acoustic target recognition, primarily due to interference from multiple vessels in real-world settings. This interference produces complex signals that contain multiple and overlapping targets. Despite the practical relevance of multi-target recognition, most existing research in underwater acoustic target recognition has concentrated on single-target recognition. To address the multi-target recognition problem, this paper proposes a deep learning (DL) framework that combines residual neural network (ResNet) architecture with channel attention modules. These modules excel in selectively filtering channel information within feature maps, amplifying informative features while mitigating noise interference. Thus, it enables accurate recognize both the quantity and types of targets within single-channel acoustic signals. In addition, this paper explores the effects of signal duration and energy ratios between different targets in mixed signals on recognition performance. Experimental results reveal that shorter signal durations hinder the extraction of discriminative features, reducing recognition accuracy. Notably, for signal durations of 5 s, the proposed model achieves a recognition accuracy of 97.16% on the dataset with energy ratios between −5 dB and 5 dB, closely approaching the accuracy of 98.09% obtained at a dataset with energy ratio of 0 dB.