Feature extraction of underwater target acoustic signals based on deep manifold learning

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (9) : 50-59.

ZHOU Yu, WANG Jin, TENG Fei, PAN Bisheng, WANG Yourui, LEI Yingke

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Abstract

With the increasing complexity of the marine environment, the data obtained from observing underwater target acoustic signals exhibits several challenging characteristics, including high dimensionality, nonlinearity, and lack of structure. These characteristics undoubtedly pose significant difficulties in extracting features from underwater target acoustic signals. In this study, a novel method for extracting features from underwater target acoustic signals is proposed, utilizing manifold autoencoders. Initially, the original data is globally optimized by leveraging the autoencoder reconstruction error to identify potential low-dimensional representations. Subsequently, the concept of preserving neighboring reconstruction weights through manifold learning is employed to enforce local constraints on the latent representation, thereby preserving its inherent topological structure. Finally, a generative adversarial network architecture is introduced for regularization, ensuring that the latent representation adheres to a specific distribution. This approach achieves a synergistic preservation of both local and global low-dimensional embedding. The proposed method was applied to extract essential features from four types of deep-water ships in the DeepShip open dataset. The quality of these features was evaluated by employing the classic classifier SVM for classification recognition. A comparison was conducted with existing methods for feature extraction in deep learning and manifold learning. The results showed an average improvement of 14.96% in recognition accuracy.

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manifold / auto-encoder / generating adversarial / feature extraction

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ZHOU Yu, WANG Jin, TENG Fei, PAN Bisheng, WANG Yourui, LEI Yingke. Feature extraction of underwater target acoustic signals based on deep manifold learning[J]. Journal of Vibration and Shock, 2024, 43(9): 50-59

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