| 深度学习在水下目标被动识别中的应用进展 |
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| 引用本文： | 徐及,黄兆琼,李琛,颜永红. 深度学习在水下目标被动识别中的应用进展[J]. 信号处理, 2019, 35(9): 1460-1475. DOI: 10.16798/j.issn.1003-0530.2019.09.003 |
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| 作者姓名： | 徐及 黄兆琼 李琛 颜永红 |
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| 作者单位： | 中国科学院声学研究所 |
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| 基金项目： | 国防科技创新特区项目；中国科学院声学研究所青年英才计划（QNYC201601） |
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| 摘 要： | 近年来,随着深度学习方法在理论上取得一系列突破性进展,其展现出相对于传统机器学习方法的明显优势。在实际应用方面,深度学习借助其出色的特征学习能力,首先在语音和图像领域取得巨大成功,并迅速引起其他领域研究者们的重点关注。本文对现阶段深度学习在水下目标被动识别领域中的国内外研究进展和应用情况进行梳理总结,包括水下目标被动识别中常用的深度神经网络结构、深度学习对特征提取环节产生的影响以及数据匮乏条件下的建模方法。针对实际应用场景所面临的挑战,本文对未来一些可能的研究方向进行了展望,供广大研究人员参考借鉴。
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| 关 键 词： | 深度学习 目标识别 被动声纳信号处理 |
| 收稿时间： | 2019-05-23 |
Advances in Underwater Target Passive Recognition Using Deep Learning |
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| Affiliation: | Institute of Acoustics, Chinese Academy of SciencesUniversity of Chinese Academy of Sciences |
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| Abstract: | In recent years, with the great breakthroughs in the theory of deep learning, it shows obvious advantages over traditional machine learning methods. By the remarkable feature learning ability, deep learning has been successfully applied to speech and image recognition, and it quickly attracted the attention of researchers in other fields. This paper summarizes the current research progress of deep learning based underwater target passive recognition methods, including the popular architectures of deep neural networks, the changes in feature extraction methods and modeling method under the condition of insufficient data. In order to meet the challenges in the future, this paper also prospects some possible research directions, which can be used for reference by relevant researchers. |
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