Research on Individual Identification of Radar Emitter Based on Multi-domain Features

Individual identification of radar emitter is an essential part in electronic warfare. With the increasing complexity of electromagnetic environment and the rapid development of electronic equipment technology, the feature extraction method based on traditional pulse descriptor has been unable to perfectly meet the requirements of current emitter identification. Based on real radar emitter signal and combined with signal transform feature extraction and automatic feature extraction of autoencoder, this paper proposes a method of extracting intra-pulse features of radar emitter based on bispectrum, wavelet packet decomposition and time-domain statistical features. Decision tree, KNN and SVM algorithms are used to classify the radar emitter based on the extracted signal features, and classification accuracy of decision tree is close to 95%, which means the individual identification of radar emitter is realized perfectly.