基于多尺度特征匹配的小样本雷达无人机识别OA

In order to improve the classification accuracy of radar-based UAV signals under limited data condi-tions,this paper proposes a few-shot classification framework EMLNet(EfficientNet-based Multi-scale Learning Net-work)based on multi-scale feature enhancement and metric learning.The method integrates an efficient multi-scale at-tention mechanism(EMA)into the lightweight EfficientNet backbone for feature extraction,which effectively enhances the stability and discriminative ability of features through multi-scale parallel sub-network with cross-spatial depen-dence modeling.In the classification stage,a local feature matching strategy is adopted to achieve fine-grained similarity modeling between the support set and the query set.To further improve training stability and generalization perfor-mance,a composite loss function(PCE Loss)that combines prototypical loss and cross-entropy loss is introduced to op-timize inter-class discrimination while maintaining intra-class feature aggregation.Experiments are carried out on the open-source Doppler radar dataset.The experimental results demonstrate that the proposed method achieves significant performance advantages in few-shot learning scenarios.