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基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别

郭立民 寇韵涵 陈涛 张明

郭立民, 寇韵涵, 陈涛, 张明. 基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别[J]. 电子与信息学报, 2018, 40(4): 875-881. doi: 10.11999/JEIT170588
引用本文: 郭立民, 寇韵涵, 陈涛, 张明. 基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别[J]. 电子与信息学报, 2018, 40(4): 875-881. doi: 10.11999/JEIT170588
GUO Limin, KOU Yunhan, CHEN Tao, ZHANG Ming. Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder[J]. Journal of Electronics & Information Technology, 2018, 40(4): 875-881. doi: 10.11999/JEIT170588
Citation: GUO Limin, KOU Yunhan, CHEN Tao, ZHANG Ming. Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder[J]. Journal of Electronics & Information Technology, 2018, 40(4): 875-881. doi: 10.11999/JEIT170588

基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别

doi: 10.11999/JEIT170588
基金项目: 

国家自然科学基金(61571146),中央高校基本科研业务费专项资金(HEUCFP201769)

Low Probability of Intercept Radar Signal Recognition Based on Stacked Sparse Auto-encoder

Funds: 

The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769)

  • 摘要: 针对低截获概率(LPI)雷达信号识别率低且特征提取困难的问题,该文提出一种基于Choi-Williams分布(CWD)和栈式稀疏自编码器(sSAE)的自动分类识别系统。该系统从反映信号本质特征的时频图像入手,首先对LPI雷达信号进行CWD时频分析,获取2维时频图像;然后对得到的时频原始图像进行预处理,并把预处理后的图像送入多层稀疏自编码器(SAE)进行离线训练;最后把SAE自动提取的特征输入softmax分类器,实现雷达信号的在线分类识别。仿真结果表明,信噪比为时,该系统对8种LPI雷达信号(LFM, BPSK, Costas, Frank和T1~T4)的整体平均识别率达到96.4%,在低信噪比条件下明显优于人工设计提取信号特征的识别方法。
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出版历程
  • 收稿日期:  2017-06-19
  • 修回日期:  2017-11-21
  • 刊出日期:  2018-04-19

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