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 presence 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.
翻译:众所周知,干扰分类在保护经授权的通信系统以及避免其在敌对环境中的性能退化方面起着重要作用。本文讨论了频率选择通信系统的干扰分类问题。考虑到频率选择系统中出现多次干扰的可能性,以便从收到的信号中充分提取干扰的有效特征,采用了线性和双线性变异复合时间频率分析方法。然后,从时间频率分析中获得的时间-频率光谱作为匹配对子和输入到深层神经网络进行分类。特别是,西亚马斯神经网络被用作分类器,将配对光谱输入深层网络的两个子网络中,这两个子网络提取了干扰类型分类的对齐光谱图特征。模拟结果证实,拟议的算法可以比传统的单一时频率代表法和亚历克斯网络传输学习或神经网络方法获得更高的分类精度。