基于SOM 聚类平滑图信号生成的MFR工作模式识别方法

UAV swarms are widely used in radar signal interception due to their advantages of wide sensing range and rapidinformation sharing. Aiming at the problem that the signal samples intercepted by UAV cluster are difficult to be fused andanalyzed directly, and the recognition accuracy of multi-function radar (MFR) working mode is low under the condition offew training samples and unbalanced working mode samples, an MFR working mode recognition method based on smoothgraph signal generated by self-organizing map (SOM) clustering is proposed. Firstly, the intercepted signal samples areclustered by using distributed SOM algorithm to extract the similarity between samples; Then, according to the clusteringresults, the signal sample set is characterized by smooth graph signal, and the correlation of signal samples under the sameworking mode is established; Finally, the graph attention network is used to fuse and classify the graph node data of the abovegraph signals to complete the MFR working pattern recognition. The experimental results show that, when the imbalanceof working mode samples is about 101 and the number of training samples in each class is 25, the recognition accuracyand F1 measure of this method are improved by 22.8% and 22.34% respectively compared with the existing methods, andcan be applied to the case of noise interference.