Statistically-Guided Dual-Domain Meta-Learning with Adaptive Multi-Prototype Aggregation for Distributed Fiber Optic Sensing

• URL: http://arxiv.org/abs/2511.17902v1
• Date: Sat, 22 Nov 2025 03:39:13 GMT
• Title: Statistically-Guided Dual-Domain Meta-Learning with Adaptive Multi-Prototype Aggregation for Distributed Fiber Optic Sensing
• Authors: Yifan He, Haodong Zhang, Qiuheng Song, Lin Lei, Zhenxuan Zeng, Haoyang He, Hongyan Wu,
• Abstract summary: We propose a novel meta-learning framework, DUPLE, for cross-deployment DFOS activity identification.<n>First, a dual-domain multi-prototype learner fuses temporal and frequency domain features, enhancing the model's generalization ability under signal distribution shifts.<n>Second, a Statistical Guided Network (SGN) infers domain importance and prototype sensitivity from raw statistical features, providing data-driven prior information for learning in unlabeled or unseen domains.<n>Third, a query-aware prototype aggregation module adaptively selects and combines relevant prototypes, thereby improving classification performance even with limited data.
• Score: 11.719957656139824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed Fiber Optic Sensing (DFOS) has shown strong potential in perimeter security due to its capability of monitoring vibration events across long distances with fine spatial resolution. However, practical DFOS systems face three critical challenges: (1) signal patterns of the same activity vary drastically under different fiber deployment types (e.g., underground, wall-mounted), causing domain shift; (2) labeled data in new deployment scenarios is often scarce or entirely unavailable, limiting model adaptability; and (3) even within source domains, data scarcity makes it difficult to capture intra-class diversity for robust learning. To address these challenges, we propose a novel meta-learning framework, DUPLE, for cross-deployment DFOS activity identification. First, a dual-domain multi-prototype learner fuses temporal and frequency domain features, enhancing the model's generalization ability under signal distribution shifts. Second, a Statistical Guided Network (SGN) infers domain importance and prototype sensitivity from raw statistical features, providing data-driven prior information for learning in unlabeled or unseen domains. Third, a query-aware prototype aggregation module adaptively selects and combines relevant prototypes, thereby improving classification performance even with limited data. Extensive experiments on cross-deployment DFOS datasets demonstrate that our method significantly outperforms baseline approaches in domain generalization settings, enabling robust event recognition across diverse fiber configurations with minimal labeled data.

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