AUTOMATIC RADAR WAVEFORM RECOGNITION BASED ON TIME-FREQUENCY ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK

被引：0
|
作者：
Wang, Chao [1 ]
Wang, Jian [1 ]
Zhang, Xudong [1 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年
关键词：
radar waveform recognition; deep learning; convolutional neural network; time-frequency image; noise reduction; MODULATION CLASSIFICATION;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper, we apply the idea of deep learning to radar waveform recognition. Since the frequency variation with time is the most essential distinction among radar signals with different modulation types, we transform one-dimensional radar signals into time-frequency images (TFIs) using time-frequency analysis and design a convolutional neural network to recognize the frequency variation patterns exhibited in TFIs. Furthermore, we analyze the statistical characteristics of the noise in TFIs and introduce a naive approach to reduce its influence on the frequency variation patterns. Simulation results demonstrate the impressive recognition rate under very low SNR conditions and the strong generalization ability of our proposed recognition method.
引用
收藏
页码：2437 / 2441
页数：5
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