TransPileSiam: multiview self-supervised pretraining with domain-adversarial regularization for few-shot specific emitter identification

Abstract

Specific emitter identification (SEI) is challenged by both limited labeled data and domain shifts caused by variations in acquisition conditions, devices, and propagation environments. To address these issues, this paper proposes TransPileSiam, a domain-aware self-supervised pretraining framework for few-shot SEI. Built upon SimSiam, the proposed method constructs multiple augmented views from each raw IQ sample and performs multi-view consistency learning to improve representation robustness under complex perturbations. To further enhance cross-domain transferability, a gradient-reversal-based domain-adversarial regularization is introduced to suppress domain-specific information in the learned features. The pretrained encoder is then adapted to downstream SEI tasks through few-shot fine-tuning. Experimental results show that TransPileSiam consistently improves downstream recognition under limited-label conditions. In particular, it improves the test accuracy of a ResNet34-based supervised model by 3.32% and yields an average gain of about 1% in few-shot evaluation. These results demonstrate that TransPileSiam effectively improves robustness, label efficiency, and cross-domain generalization for practical SEI tasks.