基于SOM聚类平滑图信号生成的MFR工作模式识别方法

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

针对无人机集群截获的信号样本难以直接融合分析,以及训练样本较少且工作模式样本不平衡条件下多功能雷达(MFR)工作模式识别精度低的问题,提出了一种基于自组织映射(SOM)聚类平滑图信号生成的MFR工作模式识别方法。首先,利用分布式SOM算法对截获的信号样本集进行聚类,提取样本之间的相似性;然后,依据聚类结果将信号样本集以平滑图信号的方式表征,建立同一工作模式下信号样本的关联;最后,采用图注意力网络对上述图信号进行图节点数据融合与分类,完成MFR工作模式识别。实验结果表明,在工作模式样本不平衡度约为10∶1,每种类别训练样本数为25时,该方法的识别准确率和F1指数相对现有方法分别提高了22.8%、22.34%,且能适用于存在一定噪声干扰的情况。

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.