Abstract
Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but poor interpretability. In this study, an attention-based neural network (ABNN) is proposed for target recognition in the pressure spectrogram with multi-source interference using an attention module to inspect the inner workings of the neural network. From data obtained during a September 2020 sea trial, the ABNN exhibited a gradual focus on the frequency-domain feature of the target ship and suppressed environmental noises and marine vessel interference, which led to high accuracy in the target detection and recognition.
Highlights
Underwater acoustic target recognition based on ship-radiated noise is a major part of sonar systems
Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets
An attention-based neural network (ABNN) is proposed for target recognition in the pressure spectrogram with multi-source interference using an attention module to inspect the inner workings of the neural network
Summary
Underwater acoustic target recognition based on ship-radiated noise is a major part of sonar systems. The performance of traditional feature extraction and classification methods is often limited by unfavorable factors, such as environmental complexity and multi-target interference.. The attention mechanism is an important method that allows for visual analysis of the inner workings of neural models.. The attention mechanism is an important method that allows for visual analysis of the inner workings of neural models.21 This mechanism, which was proposed in natural language processing, imitates and demonstrates the human visual and auditory attention mechanism to focus the attention on the main aspects of items.. An attention-based neural network (ABNN) is proposed for the underwater acoustic target recognition in an attempt to interpret the classification principle of DNN. The ABNN architectures include an attention module as a first step prior to a traditional DNN composed of connected layers.
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