Aggregated-attention deformable convolutional network for few-shot SAR jamming recognition

Abstract

This work simultaneously addresses the challenges of unseen classes and low-data problems on synthetic aperture radar jamming recognition (SAR-JR). Currently, very few studies have tackled both challenges. Inspired by the success of few-shot learning, which learns a robust model from a few instances, we formulate SAR-JR as a few-shot task in a metric-learning framework to alleviate the above challenges. Against the jamming features with significant dispersion and complex geometric transformations, as well as feature obscuration in time–frequency images (TF), we propose an aggregated-attention deformable convolutional network (A2-DCNet) framework consisting of an aggregated-attention deformable convolutional module (A2-DC-Module) and a prototype classification module based on polynomial loss (PolyLoss-PC-Module). The former learns informative and refined embeddings from the TF images, while the latter performs the SAR-JR in an embedding space by calculating distances to prototypes of each class. Specifically, the modulated deformable convolution of the A2-DC-Module can capture long-range spatial contextual information from a global perspective, while the aggregated attention is designed to refine the representations of obscured features in the TF images. To further optimize the framework, we introduce a novel PolyLoss and customize the optimal form for our model to learn an embedding space with robust inter-class separability. Finally, to realize few-shot SAR-JR tasks, we simulate a novel dataset called JamSet. Extensive experimental results on our dataset have demonstrated substantial improvement of our proposed A2-DCNet method over the benchmarks.