小样本条件下的通信辐射源半监督特征提取

在通信辐射源信号有标签样本数量较小的情况下,同类通信辐射源个体信号特征提取困难且识别精度较低。对此,提出了一种小样本条件下的通信辐射源半监督特征提取方法。该方法对少量有标签通信辐射源信号样本以及大量无标签通信辐射源信号样本进行变分模态分解提取高维稳态信息熵,利用指数半监督判别分析法映射信息熵形成个体特征,并通过XGBoost进行通信辐射源个体识别来验证识别效果。实验表明,所提方法识别准确率达到85.33%,相比无监督特征提取方法运算时间降低了76.17%,证明其在同类通信辐射源不同个体识别中具有较好的性能。

Semi-supervised feature extraction of communication emitter under small sample condition

In the case of few label samples of the communication emitter signal, it is difficult to extract individual features of similar emitter signals and the identification accuracy is low. To this regard, a semi-supervised feature extraction method of communication emitter under the small sample condition is proposed. A small number of labeled emitter signal samples and a large number of unlabeled emitter signal samples are subjected by variational mode decomposition to extract high-dimensional steady-state information entropy. The exponential semi-supervised discriminant analysis is used to map the information entropy to form individual features. In addition, XGBoost is used to identify the communication emitter to verify the identification effect. Experiments show that the proposed method reduces the computation time by 76.17% compared with the unsupervised feature extraction method, and the identification rate reaches 85.33%, which proves that it has better performance in different individual identification of similar communication emitters.