The research of interference recognition method in multi-nodes cooperative frequency-hopping communication based on time-frequency image analysis and deep learning

Interference recognition has a significant and irreplaceable importance in keeping battlefield communication reliability and securing information safety. However, the existing recognition algorithms have a common disadvantage, that the model scale is normally very huge. In order to satisfy the demand of decreasing mobile device weight and limiting the memory space, this article designed a lightweight neural network based on the idea of structure reparameterization. This network will take the time-frequency image of the interference signal as the input of the convolutional neural network (CNN), and import the efficient channel attention module, efficiently extract the feature of the interference signals, thus benefiting the interference recognition. The simulation experiment indicates that the network has a high accuracy rate in interference recognition in the circumstances when the quantity of parameters is not huge. At the same time, in order to solve the problem of low recognition accuracy due to the limit of information, this article designed an interference recognition algorithm based on multi-nodes collaboration technology. Considering jamming-to-noise ratio (JNR) could differ for each node, this article set a node select module, that could select the nodes that contain a higher result of JNR as the input of the fusion algorithm when each JNR is highly different, which could adjust abnormal influence from each node. The simulation experiment indicates that, the interference recognition algorithm based on multi-nodes collaboration technology could efficiently increase interference recognize accuracy, and reduce the influence from low JNR nodes.