Enhancing HRRP RATR Robustness to Incomplete Aspect Angles via Supervised Contrastive Learning

High Resolution Range Profile (HRRP) is crucial for Radar Automatic Target Recognition (RATR), but its sensitivity to target aspect angle significantly degrades performance when observational data is incomplete across angles. Traditional recognition methods relying on manually designed features or standard deep learning struggle with this aspect variation, limiting generalization. To address this challenge, this paper proposes SupConResNet, a deep recognition method leveraging Supervised Contrastive Learning (SCL). SupConResNet integrates a 1D ResNet architecture tailored for HRRP signals with a dual-objective loss function utilizing supervised contrastive learning mechanism. By explicitly using label information, the SCL component encourages the learning of feature representations that are not only discriminative between different target classes but also robust to intra-class variations caused by differing aspect angles. We conduct systematic experiments on an electromagnetic simulation dataset, specifically evaluating performance under scenarios simulating incomplete aspect angle coverage, including both cross-angle generalization and coarse angular sampling. The results demonstrate that SupConResNet effectively maintains high recognition accuracy even with significant angular gaps between training and testing data, significantly outperforming implicitly baseline approaches reliant solely on classification loss.