MFR working mode recognition based on smooth graph signal generated by SOM clustering

UAV swarms are widely used in radar signal interception due to their advantages of wide sensing range and rapid information sharing. Aiming at the problem that the signal samples intercepted by UAV cluster are difficult to be fused and analyzed directly, and the recognition accuracy of multi-function radar (MFR) working mode is low under the condition of few training samples and unbalanced working mode samples, an MFR working mode recognition method based on smooth graph signal generated by self-organizing map (SOM) clustering is proposed. Firstly, the intercepted signal samples are clustered by using distributed SOM algorithm to extract the similarity between samples; Then, according to the clustering results, the signal sample set is characterized by smooth graph signal, and the correlation of signal samples under the same working mode is established; Finally, the graph attention network is used to fuse and classify the graph node data of the above graph signals to complete the MFR working pattern recognition. The experimental results show that, when the imbalance of working mode samples is about 10 ∶1 and the number of training samples in each class is 25, the recognition accuracy and F1 measure of this method are improved by 22.8% and 22.34% respectively compared with the existing methods, and can be applied to the case of noise interference.