辐射源个体识别的一种可解释性测试架构

由于射频信号种类多，电磁环境复杂，特征提取难度大，现有的基于人工特征的射频辐射源个体识别方法的鲁棒性、适用性难以满足应用需求。数据驱动的深度学习方法虽然可以提供更灵活的辐射源个体识别模式，但深度学习方法自身可解释性差，而且缺乏通用测试模式来评价一个深度学习方法的优劣。本文在电磁大数据非凡挑战赛目标个体数据集的基础上，探索了基于该数据集的深度学习模型测试方法，提出面向辐射源个体识别神经网络模型的通用测试系统架构。该构架通过信号特征遮掩、生成对抗网络(GAN)、欺骗信号汇集、信道模拟等方法构造仿真测试样本，并把测试样本与原样本数据导入深度模型进行识别结果对比测试。基于测试结果分析了深度模型聚焦的信号关键特征位置，分析模型的鲁棒性，揭示信道环境对识别性能的影响，从而解释了深度学习网络模型的性能。

An interpretable testing architecture for specific emitter identification

Due to the diversity of RF signals, the complexity of the electromagnetic environment, and the difficulty of feature extraction, the robustness and applicability of the existing artificial features-based RF-specific emitter identification methods cannot meet the application requirements. Although the data-driven deep learning methods can provide a more flexible mode of specific emitter identification, they are less interpretable and lack a general test mode to evaluate their advantages and disadvantages. An evaluation method is explored for the deep learning model on the target individual dataset of the Electromagnetic Big Data Super Contest, and a general testing system architecture is proposed for the specific emitter identification model based on deep neural networks. The framework constructs the simulation test samples through signal feature masking, Generative Adversarial Network (GAN), deception signal collection, channel simulation and other methods, and imports the test samples and original data into the deep model to compare the recognition results. The test results are employed to judge the location of the signal key features extracted by the deep model, to analyze the robustness of the model, and to reveal the impact of the channel environment on the recognition performance, thus the performance of the deep learning model can be interpretable.