Lightweight Network Design for Joint Detection and Modulation Recognition of LPI Radar Signals with Knowledge Distillation

In the field of electronic support and radar warning, it is necessary to effectively detect and recognize the modulation types of non-cooperative radar signals, especially for radars with Low Probability of Intercept (LPI) waveforms. Multiple intelligent detection and recognition algorithms based on the Transformer architecture have been proposed, achieving good performance even under low signal-to-noise ratio (SNR). However, Transformer-based radar intelligent detection and recognition algorithms have a huge number of parameters coupled with complex structures, which will result in significant power consumption and computational latency when deployed on general computing platforms. To address the above issues, this paper proposes a lightweight design for Transformer-based radar signal intelligent detection and recognition networks. A Lightweight Joint Detection and Modulation Recognition Networks (JDMR-LNet) is designed. To enhance the feature extraction ability of lightweight networks, this paper designed a hybrid model distillation method. The experimental results demonstrate that, compared with the directly trained JDMR-LNet, the accuracy of automatic modulation type recognition of the JDMR-LNet after distillation is increased by 2.37% at −12 dB, and the signal detection is increased by 2.07% at −10 dB. The number of parameters of the JDMR-LNet has also decreased significantly. Compared with the original model, the JDMR-LNet is compressed by 11.18 times. Furthermore, this paper completed FPGA deployment of the JDMR-LNet model, with simulation verifying its functional correctness.