Underwater acoustic target recognition based on multi-resolution wavelet feature deep learning

The application of underwater acoustic target recognition is extensive, exploration of marine resources, submarine vehicle detection and so on. However, traditional feature extraction methods are susceptible to the influence of complex marine environments and target operating conditions, leading to a significant reduction in the correct recognition rate. This paper introduces an underwater acoustic target recognition method that integrates multi-resolution wavelet features with deep learning algorithms, aiming to overcome the limitations of traditional time-frequency analysis techniques, which are unable to extract multiple signal characteristics simultaneously due to the trade-off between time and frequency resolution. Specifically, the essence of this technique is twofold: (1) Selecting suitable wavelet bases and decomposition levels to capture signal characteristics at different resolutions, effectively seizing the signal's local features within the time-frequency domain; (2) Fusing signal features across different resolutions to optimize the feature extraction process and enhance the distinguishability of target features. Finally, by applying deep learning algorithms to experimentally measured underwater acoustic data, the results demonstrate that this method can effectively enhance the accuracy of underwater acoustic target recognition.