基于深度残差收缩网络的辐射源个体识别方法

辐射源个体识别是电子对抗领域中的重要技术，通过识别设备间不同细微特征从而达到区分非法设备与合法设备的目的。针对辐射源个体间指纹特征差异细微且在噪声干扰下提取特征较少的问题，本文提出了一种基于深度残差收缩网络的辐射源个体识别方法。该方法首先将I/Q图特征数据进行拼接，利用数据增强技术进行样本扩充，进而构建了深度残差收缩网络识别模型，最后对构建的模型进行ADS-B辐射源个体识别训练并进行识别效果评估。仿真结果表明，本文构建的深度残差收缩网络通过消除数据噪声的优势，对数据增强后的20类ADS-B辐射源个体在0dB的低信噪比条件下总体识别准确率达到98.2%，其性能较相同层数的Resnet网络提高了1.3%，并明显优于现有其他方法。

Individual Radiator Identification Method Based on Deep Residual Shrinkage Network

The identification of individual radiation sources is an important technology in the field of electronic countermeasures. By identifying different subtle features between devices, the purpose of distinguishing illegal devices from legal devices is achieved. Aiming at the problem of subtle differences in fingerprint features between individual radiation sources and fewer features extracted under noise interference, this paper proposes a method of identifying individual radiation sources based on a deep residual shrinkage network. This method first splices the feature data of the I/Q map, uses data enhancement technology to expand the sample, and then constructs a deep residual shrinkage network recognition model. Finally the constructed model is trained for individual ADS-B radiation source recognition and the recognition effect is evaluated. The simulation results show that the deep residual shrinkage network constructed in this paper uses the advantage of eliminating data noise, and the overall recognition accuracy of the 20 types of ADS-B radiation source individuals after data enhancement has reached 98.2% when the SNR is as low as 0 dB.Compared with the Resnet network with the same number of layers, its performance is improved by 1.3%, and it is significantly better than other existing methods.