Phased array radar individual recognition based on phase-frequency fusion feature

To solve the problem of phased-array radar individual identification in the complex electromagnetic environment with wide spectrum, heterogeneous waveforms and strong energy, an unintentional modulation feature extraction method based on phase and frequency fusion was proposed for the individual features carried by phased-array radar signals. Considering that the phased array radar signal was difficult to collect, the number of transceiver components was large, and the unintentional modulation features were complex, the phased array radar unintentional modulation signal model was constructed based on the method of wave-position orchestration and isophase surface. Based on the bispectral method, the signal bispectral map was obtained and perimeter integration was performed to extract the unintentionally modulated phase features of the signal. Based on the variational modal decomposition method, the original signal was decomposed to obtain the modal components, and the energy ratio difference of the set of modal components was further computed to extract the unintentionally modulated frequency features of the signal. Finally, the local holding projection method was used to integrate the phase and frequency features, and the K nearest-neighbor classification method was adopted based on the tree retrieval method to realize the individual identification. Representative numerical results are reported, indicating that the proposed method has higher recognition accuracy and efficiency.