Generalizable Underwater Acoustic Target Recognition Using Feature Extraction Module of Neural Network
Source abstract
Abstract
The underwater acoustic target signal is affected by factors such as the underwater environment and the ship’s working conditions, causing the generalization of the recognition model is essential. This study is devoted to improving the generalization of recognition models, proposing a feature extraction module based on neural network and time-frequency analysis, and validating the feasibility of the model-based transfer learning method. A network-based filter based on one-dimensional convolution is built according to the calculation mode of the finite impulse response filter. An attention-based model is constructed using the convolution network components and full-connection components. The attention-based network utilizes convolution components to perform the Fourier transform and feeds back the optimization gradient of a specific task to the network-based filter. The network-based filter is designed to filter the observed signal for adaptive perception, and the attention-based model is constructed to extract the time-frequency features of the signal. In addition, model-based transfer learning is utilized to further improve the model’s performance. Experiments show that the model can perceive the frequency domain features of underwater acoustic targets, and the proposed method demonstrates competitive performance in various classification tasks on real data, especially those requiring high generalizability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Underwater Acoustics Research · Speech and Audio Processing · Music and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Innovation Institute of Defense Technology, Chinese Academy of Military Science, Beijing 100091, China
Institute of Ocean Engineering and Technology, Zhejiang University, Zhoushan 316000, China
College of Electronic Information Engineering, Beihang University, Beijing 100190, China
References (49)
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