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水下高速目标声谱图特征提取及分类设计

王森 王余 王易川 李海涛

王森, 王余, 王易川, 李海涛. 水下高速目标声谱图特征提取及分类设计[J]. 电子与信息学报, 2017, 39(11): 2684-2689. doi: 10.11999/JEIT170283
引用本文: 王森, 王余, 王易川, 李海涛. 水下高速目标声谱图特征提取及分类设计[J]. 电子与信息学报, 2017, 39(11): 2684-2689. doi: 10.11999/JEIT170283
WANG Sen, WANG Yu, WANG Yichuan, LI Haitao. Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2684-2689. doi: 10.11999/JEIT170283
Citation: WANG Sen, WANG Yu, WANG Yichuan, LI Haitao. Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2684-2689. doi: 10.11999/JEIT170283

水下高速目标声谱图特征提取及分类设计

doi: 10.11999/JEIT170283

Feature Extraction and Classification of Spectrum of Radiated Noise of Underwater High Speed Vehicle

  • 摘要: 为了增加水下高速目标的识别特征维度,优化识别效果,该文设计了一种基于目标辐射噪声高速特征量(High Speed Characteristic Quantity, HSCQ)的分类方法。首先,针对水下高速目标辐射噪声的DEMON(Detection of Envelope Modulation On Noise)谱特征进行分析,根据DEMON谱的频率可分性,定义了DEMON谱调制分布比(Modulation Distribution Ratio, MDR)。然后,根据水下高速目标辐射噪声的功率谱历程图直纹特征,应用图像边缘检测、线谱生长等理论进行特征提取,并分析了功率谱历程图的直纹特征量(Straight-line Characteristic Quantity of Spectrum, SCQS)。最后,根据2种特征量的实测信号分析结果,定义了目标辐射噪声的HSCQ,实现了一种新的水下高速目标分类方法。实测信号分析结果显示,采用MDR或SCQS进行单特征量分类,非高速目标的误报率分别为21.4%和16.3%;采用HSCQ进行分类,非高速目标的误报率仅为4.1%。
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出版历程
  • 收稿日期:  2017-04-01
  • 修回日期:  2017-08-25
  • 刊出日期:  2017-11-19

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