  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.
