Few-shot jamming recognition based on NMF combined with multi-dimensional fusion network

Abstract

Accurately identifying specific types of active jamming is essential for optimizing radar resources and enhancing anti-jamming efficiency, particularly in the context of few-shot sample sizes, as discussed in this study. We first employ non-negative matrix factorization (NMF) to pre-process the radar signal. NMF enhances the feature representation of data while simultaneously augmenting the sample size. Subsequently, we propose a multi-dimensional fusion network (MDFN) designed to integrate high-dimensional features and classify jamming signals effectively. The proposed method demonstrates superior performance compared to existing approaches across twelve categories of jamming in few-shot scenario. Experimental results are presented to validate the reliability and effectiveness of the proposed method.