KEYWORDS: Radar, Performance modeling, Convolution, Signal to noise ratio, Modulation, Continuous wavelet transforms, Signal processing, Radar signal processing, Detection and tracking algorithms, Time-frequency analysis
For the problem that traditional radar intra-pulse signals identification needs expert knowledge, a method of radar intra-pulse signals identification based on Choi-Williams Distribution (CWD) and Convolutional Neural Network (CNN) is proposed in this work. Firstly, the characteristics of the collected radar intra-pulse signals are acquired. Then, the feature images of these characteristics are preprocessed. Finally, the intelligent identification of radar intra-pulse signals based on CNN is realized. The experimental results show that when the Signal to Noise Ratio (SNR) is 5dB, the identification accuracy of algorithm model proposed in this work based on CWD and CNN can reach 87.95%, while that based on Continuous Wavelet Transform (CWT) and CNN can only reach 72.23%. The significance of this work further optimizes the feature extraction of radar intra-pulse signals and provides an empirical reference for radar intelligent identification in EW.
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