基于领域自适应的动态噪声辐射源个体识别

现有基于深度神经网络的辐射源识别算法受训练场景限制,当待测信号与训练数据集的信道环境噪声不一致时,网络的识别性能严重退化。为了克服该问题,本文提出一种基于迁移学习的辐射源个体识别算法。该算法结合领域自适应的思想,建立优化模型将不同信噪比下信号的特征对齐,使在特定信噪比下训练的神经网络学习到与信道噪声无关的射频指纹特征,实现对其他信噪比下信号的高准确率识别。仿真实验结果表明,提出的算法显著提升了基于深度神经网络的辐射源个体识别算法在动态噪声条件下的准确率,在待识别信号信噪比下降4 dB的情况下,准确率提升了45.18%。 

Specific Emitter Identification under Dynamic Noise Based on Domain Adaptation

Existing deep neural network (DNN) based specific emitter identification (SEI) algorithms are limited by training scenarios, and the identification accuracy of the network is seriously degraded when the signal to be identified is not consistent with the channel noise of the training data. In order to solve this problem, this paper proposes a SEI algorithm based on transfer learning. Combining with the idea of domain adaptation, this method established an optimization model to align the features of signals under different signal-to-noise ratio (SNR), so that the neural network trained under a specific SNR can learn the radio frequency fingerprint (RFF) features which are independent of channel noise, and realize the identification of signals under other SNR conditions with high accuracy. Simulation results show that the proposed algorithm improves the accuracy of the SEI algorithm based on DNN under the interference of dynamic noise. When the SNR of the signal to be identified decreases by 4dB, the identification accuracy can be improved by 45.18%.