Underwater Acoustic Target Recognition Based on Area Weighted GWT-GFT
Due to the complexity of the marine environment,the recognition of underwater acoustic targets poses significant challenges.To address the problem of feature extraction in such complex environments,an analysis method based on area weighted Graph Wavelet Transform-Graph Fourier Transform(GWT-GFT)is proposed.After completing data preprocessing,a novel weighted method based on the triangle area of vertices is proposed to construct the graph signal in order to highlight the relationships between vertices.The con-structed graph signal is decomposed into multiple-scale graph components using GWT.Then,these components are transformed from the graph domain to the eigenvalue spectrum domain for analysis using GFT.Based on this,the characteristic eigenvalue spectra of each com-ponent are extracted.Finally,the obtained feature vectors are classified using Support Vector Machine(SVM)based on the Gaussian kernel function.Based on the ShipsEar database of underwater acoustic signals,a 5-fold cross-validation method is employed for verification.Compared with other existing methods,the proposed model achieves a recognition accuracy of 97.22%on a dataset consisting of 376 656 samples using 36 features.This result demonstrates the effectiveness and robustness of the proposed analysis meth-od.
underwater acoustic target recognitiongraph wavelet transform-graph Fourier transformfeature extractiongraph signal pro-cessingvertex triangle area weighting
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