A Study on Radar Intelligent Jamming Modulation Type Classification Based on Multi-node Strategy

The cognitive radar architecture contributes to enhancing the intelligence of radar anti-jamming technology. Addressing the issue of radar perception of electromagnetic interference in the field of cognitive anti-jamming technology, a multi-node jamming modulation type recognition method based on deep learning is proposed. This method targets various nodes in radar signal processing, such as digital beamforming, adaptive sidelobe cancellation, before and after pulse compression, and post-moving target detection. The time-frequency plane and range-Doppler plane of multiple nodes are used as joint feature extraction objects for jamming signals. A deep learning-based multi-node jamming recognition strategy model is established to improve jamming recognition accuracy in various scenarios. To enhance the extraction capability of jamming features and the training efficiency of the network, the deep learning algorithm for jamming recognition incorporates attention mechanisms and residual networks into the convolutional neural network. This establishes an interference type recognition network structure for multi-node strategies, achieving recognition of various jamming types in different scenarios. Simulation results show that in a single jamming scenario, the proposed algorithm achieves a jamming recognition accuracy of up to 92?? at a jamming-to-noise ratio (JNR) of 14 dB. In multiple jamming scenarios, the proposed algorithm, supported by different node strategies, achieves an accuracy of up to 90%.