Transfer Learning-Based Dual GCN for Radar Active Deceptive Jamming Few-Shot Recognition

Abstract

In the context of high-dynamic electronic warfare, improving the precision of radar active jamming recognition (JR) algorithms under small sample conditions has become a research hotspot. When confronted with scarcity of labeled data and low jamming-to-noise ratio (JNR), existing intelligent models struggle to extract sufficient discriminative features, leading to suboptimal recognition accuracy. Particularly in the current environment of diverse and variable jamming types, these models exhibit inadequate robustness in jamming differentiation. To address these issues, this article integrates transfer learning (TL) and dual graph convolutional network (DGCN) to propose a few-shot learning (FSL) method for radar active deception JR. First, this method utilizes the large mini-ImageNet dataset to assist in pretraining a basic FSL model, obtaining abundant transferable general knowledge while excavating specific information about jamming from limited annotated samples through TL, thereby enhancing the generalization capability and optimizing efficiency of the model. Subsequently, to further improve the robustness of the model in recognizing diverse and complex jamming types, a DGCN structure is integrated into the fine-tuning of the FSL model based on deep convolutional architecture. It models the nonlocal correlations among jamming using a feature graph and distribution graph, facilitating the effective propagation of interclass knowledge interaction and label information through message aggregation and propagation, making jamming features more similar within classes and more distinguishable between classes. Finally, the features extracted by the deep convolutional structure and DGCN are fused to obtain more comprehensive and enriched feature representation for accurate JR. Experimental results on typical jamming datasets demonstrate the effectiveness and superiority of the proposed recognition method under the condition of small sample and low JNRs.