基于深度流形学习的水中目标声信号特征提取

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摘要随着海洋环境日益复杂，水中目标声信号观测数据呈现出高维数、非线性、非结构化等特点，无疑给水中目标声信号特征提取带来严峻挑战，该文提出了一种基于流形自编码器的水中目标声信号特征提取方法。首先，通过自编码器重建误差对原始数据进行全局优化，找出潜在的低维表示结果；然后，利用流形学习保持近邻重构权值的思想对潜在表示实施局部约束，保留其内在拓扑结构；最后，引入生成对抗网络架构进行正则化处理，使潜在表示服从特定分布，从而实现一种局部与全局的联合保持低维嵌入方法。在DeepShip深水船公开数据集上进行实验，使用该文方法提取4种深水船数据的本质特征，为评估该类特征的质量水平，利用经典分类器SVM进行分类识别，与现有深度学习以及流形学习特征提取方法对比，识别精度平均提高14.96%。

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	周钰
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	潘必胜
	王友瑞
	雷迎科

关键词 ：流形, 自编码, 生成对抗, 特征提取

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.

Key words： manifold auto-encoder generating adversarial feature extraction

收稿日期: 2023-06-26 出版日期: 2024-05-15

引用本文:

周钰，王津，滕飞，潘必胜，王友瑞，雷迎科. 基于深度流形学习的水中目标声信号特征提取[J]. 振动与冲击, 2024, 43(9): 50-59.
ZHOU Yu, WANG Jin, TENG Fei, PAN Bisheng, WANG Yourui, LEI Yingke. Feature extraction of underwater target acoustic signals based on deep manifold learning. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(9): 50-59.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I9/50

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