| 程玉胜,邱家兴,刘振,李海涛.水声被动目标识别技术挑战与展望[J].,2019,38(4):653-659 |
| 水声被动目标识别技术挑战与展望 |
| Challenge and prospects of underwater acoustic passive target recognition technology |
| 投稿时间：2019-02-22 修订日期：2019-07-03 |
| 中文摘要: |
| 低频水声探测和船舶减振降噪技术发展，使得传统水声目标识别技术性能逐渐下降。该文分析了声呐工作带宽、探测频率、船舶减振降噪给识别技术带来的挑战。针对低频声呐广泛使用的低频线谱识别，研究了低频线谱的识别能力问题；针对智能识别技术发展，研究了深度学习技术在船舶辐射噪声识别中的应用问题，并给出了数据试验结果，文章最后指出了水声被动目标识别技术亟需开展的研究内容和方向。 |
| 英文摘要: |
| With the development of low frequency underwater acoustic detection and the technology of ship vibration and noise reduction, the performance of traditional underwater acoustic target recognition technology is gradually declining. This paper analyses the challenges brought by sonar bandwidth, detection frequency, ship vibration and noise reduction to identification technology. Aiming at the recognition of low frequency line spectrum widely used in low frequency sonar, the recognition ability of low frequency line spectrum is studied. Aiming at the development of intelligent recognition technology, the application of deep learning technology in the recognition of ship radiated noise is studied, and the data test results are given. Finally, the contents and directions of the research on underwater acoustic passive target recognition technology are pointed out. |
| DOI：10.11684/j.issn.1000-310X.2019.04.023 |
| 中文关键词: 被动声呐识别，水声低频探测，船舶减振降噪，深度学习，识别特性 |
| 英文关键词: Passive sonar target recognition, Underwater acoustic low frequency detection, Ship vibration and noise reduction, Deep learning, Recognition characteristics |
| 基金项目: |
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