RADAR WAVEFORM RECOGNITION USING FOURIER-BASED SYNCHROSQUEEZING TRANSFORM AND CNN

被引:0
|
作者
Kong, Gyuyeol [1 ]
Koivunen, Visa [1 ]
机构
[1] Aalto Univ, Dept Signal Proc & Acoust, Espoo, Finland
来源
2019 IEEE 8TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2019) | 2019年
关键词
Choi-Williams distribution; convolutional neural network; Fourier-based synchrosqueezing transform; radar waveform recognition;
D O I
10.1109/camsap45676.2019.9022525
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing time-varying behavior, rate of, strength and number of oscillatory components in received signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing the polyphase waveforms even at low signal-to-noise ratio regime.
引用
收藏
页码:664 / 668
页数:5
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