Abstract：A radar signal recognition method of low probability of intercept (LPI) based on the stacked autoencoder and the support vector machine is proposed. First, the signal is transformed to the time-frequency (T-F) domain to obtain the T-F images through the ChoiWilliams Distribution. Second, image processing methods are used to process the T-F images. Then, the stacked autoencoder is used to extract features from the preprocessed images. Finally, the support vector machine (SVM) is used to recognize the signal. The method uses the arbitrary waveform generator (AWG) to generate eight kinds of LPI radar signals and uses the stacked autoencoder combined with the SVM to recognize the signal. Simulation results show that the method can effectively classify the LPI radar signal in low SNR and small sample situations.
张穆清, 王华力, 倪雪. 基于深度学习与支持向量机的低截获概率雷达信号识别[J]. 科技导报, 2019, 37(4): 69-75.
ZHANG Muqing, WANG Huali, NI Xue. The LPI radar signal recognition based on deep learning and support vector machine. Science & Technology Review, 2019, 37(4): 69-75.
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