It is known that interference classification plays an important role in protecting the authorized communication system and avoiding its performance degradation in the hostile environment. In this paper, the interference classification problem for the frequency hopping communication system is discussed. Considering the possibility of the presence of multiple interferences in the frequency hopping system, in order to fully extract effective features of the interferences from the received signals, the linear and bilinear transform-based composite time-frequency analysis method is adopted. Then, the time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input to the deep neural network for classification. In particular, the Siamese neural network is used as the classifier, where the paired spectrograms are input into the two sub-networks of the deep networks, and these two sub-networks extract the features of the paired spectrograms for interference-type classification. The simulation results confirm that the proposed algorithm can obtain higher classification accuracy than both traditional single time-frequency representation-based approach and the AlexNet transfer learning or convolutional neural network-based methods.

众所周知，干扰分类在保护授权通信系统和避免其在恶劣环境中的性能下降方面起着重要作用。本文讨论了跳频通信系统的干扰分类问题。考虑到跳频系统中可能存在多重干扰，为了从接收信号中充分提取干扰的有效特征，采用基于线性和双线性变换的复合时频分析方法。然后，将时频分析得到的时频谱图构造为匹配对，输入深度神经网络进行分类。特别是使用Siamese神经网络作为分类器，其中成对的频谱图被输入到深度网络的两个子网络中，这两个子网络提取成对频谱图的特征进行干扰类型分类。仿真结果证实，与传统的基于单时频表示的方法和基于 AlexNet 迁移学习或卷积神经网络的方法相比，所提出的算法可以获得更高的分类精度。