基于转换脉冲神经网络的雷达辐射源识别方法

为提高雷达辐射源识别智能水平，提出一种新的基于转换脉冲神经网络进行雷达辐射源调制模式识别的 方法。将仿真产生的雷达信号转换为2 维时频图，将传统的卷积神经网络(convolutional neural networks, CNN)转化 为脉冲神经网络(spiking neuron network, SNN)，使用SNN 进行雷达辐射源识别。仿真实验结果表明：该方法具有 优良的检测精度，当信噪比高于-9 dB 时，识别概率可达96%以上。

Radar Emitter Recognition Method Based on Converted Spiking Neural Network

In order to improve the intelligence level of radar emitter recognition, a new method of radar emitter modulation pattern recognition based on converted spiking neural network is proposed. The simulated radar signal is transformed into a 2D time-frequency map, and the traditional CNN (convolutional neural networks) is transformed into a SNN (spiking neuron network), which is used for radar emitter recognition. The simulation results show that the proposed method has excellent detection accuracy, and the recognition probability can reach more than 96% when the SNR is higher than -9 dB.