Distributed Fiber Optic Sensing (DFOS) is promising for long-range perimeter security, yet practical deployment faces three key obstacles: severe cross-deployment domain shift, scarce or unavailable labels at new sites, and limited within-class coverage even in source deployments. We propose DUPLE, a prototype-based meta-learning framework tailored for cross-deployment DFOS recognition. The core idea is to jointly exploit complementary time- and frequency-domain cues and adapt class representations to sample-specific statistics: (i) a dual-domain learner constructs multi-prototype class representations to cover intra-class heterogeneity; (ii) a lightweight statistical guidance mechanism estimates the reliability of each domain from raw signal statistics; and (iii) a query-adaptive aggregation strategy selects and combines the most relevant prototypes for each query. Extensive experiments on two real-world cross-deployment benchmarks demonstrate consistent improvements over strong deep learning and meta-learning baselines, achieving more accurate and stable recognition under label-scarce target deployments.

翻译：分布式光纤传感（DFOS）在大范围周界安防领域具有广阔前景，但其实际部署面临三大关键障碍：严重的跨部署域偏移、新站点标签稀缺甚至缺失，以及源部署中类内覆盖范围有限。我们提出DUPLE——一种专为跨部署DFOS识别设计的基于原型的元学习框架。其核心思想是联合利用时频域互补线索，并根据样本特定统计信息调整类别表示：（i）双域学习器构建多原型类别表示以覆盖类内异质性；（ii）轻量级统计引导机制根据原始信号统计信息评估各域可靠性；（iii）查询自适应聚合策略为每个查询选择并组合最相关原型。在两个真实世界跨部署基准测试上的大量实验表明，与强深度学习及元学习基线相比，该方法在标签稀缺的目标部署场景下实现了更准确且稳定的识别，表现出一致性提升。