一种深度学习的雷达辐射源识别算法

Radar emitter identification algorithm based on deep learning

针对传统依靠于人工经验提取雷达辐射源特征方法的不足,提出了一种新颖的基于联合深度时频特征的辐射源识别算法.首先将时域信号变换到二维时频域,并利用随机投影和主成分分析方法分别从维持子空间和能量角度对时频图像降维;接着在预训练阶段,利用无标签的样本信号层级训练深度模型,再根据类别信息精调网络参数;最后,构造了逻辑回归分类来完成识别任务.仿真实验中利用6种辐射源信号验证了提出算法的有效性,结果表明,联合深度特征更加有助于提高识别准确度,算法运行更加高效.

Aimed at the deficiency of traditional techniques of radar emitter feature extraction which rely heavily on artificial experience, a novel emitter identification algorithm based on joint deep time-frequency features is proposed. Time-domain signals are transformed into the 2-D time-frequency domain, and dimensionality reduction is implemented with random projection and principal component analysis with respect to sustaining subspace and energy. In the phase of pre-training, the deep model is layer-wise trained with unlabelled samples and network parameters are fine-tuned with label information. Finally the identification task is achieved with a logistic regression classifier. 6 types of emitter signals are adopted in simulation experiments to validate the effectiveness of the proposed algorithm, the experimental results indicating that the joint deep features help to obtain higher identification accuracy and that the algorithm is more efficient.