A Masked Representation Alignment-Based Self-Supervised Learning Method for Radar Emitter Recognition

Radar emitter recognition based on pulse description words (PDWs) serves as a fundamental prerequisite for target identification and tracking in electronic support measures (ESM). Although self-supervised learning (SSL) has been widely applied to text and image tasks, few effective SSL paradigms are available for the feature representation of radar PDWs. In this paper, a novel masked representation alignment-based self-supervised learning (SSL-MRA) method is proposed for radar emitter feature learning and recognition. Firstly, a dual-branch Transformer encoder is designed to extract contextual representations from both masked and unmasked tokens. Secondly, a cross-attention Transformer-based predictor is constructed to recover the masked representations from unmasked features. Furthermore, a codebook-based tokenizer is developed to learn discrete representations of masked inputs. Based on the masked prediction mechanism, two pretext tasks are established for the pre-training of SSL-MRA. Specifically, a masked discrete representation alignment task is adopted to replace traditional reconstruction-based pre-training, which implements codebook classification for semantic discretization. Meanwhile, a masked prediction representation alignment task is constructed to constrain the high-dimensional semantic consistency of feature embeddings. Experimental results on both simulated and real measured datasets demonstrate that the proposed method achieves superior feature representation capability. It yields an average recognition accuracy of 94.95% on source-domain data, outperforming all baseline methods. Moreover, the proposed SSL-MRA also achieves better cross-domain transfer performance than the mainstream masked autoencoder method.