Few-Shot Target Recognition in Foliage Environments Across Different Weather Conditions via Meta-Learning

Accurate and robust foliage penetration (FOPEN) target recognition is crucial for practical outdoor sensing applications. Compared to traditional sensing technologies, Device-Free Sensing (DFS) has emerged as a promising solution due to its low deployment cost. However, existing DFS-based approaches suffer from significant performance degradation under cross-weather domain shifts and typically rely on extensive relabeling or online fine-tuning when climate conditions change. Such dependence on parameter updating severely limits their applicability in dynamic foliage environments. To overcome this limitation, we propose PNFOPENet, a meta-learning framework that employs Prototypical Networks (PN) to enable FOPEN recognition across different weather conditions using minimal labeled samples, without requiring any model parameter updates. Specifically, the model is trained through episodic meta-learning tasks under given weather conditions to learn class prototypes using a small number of samples. This facilitates the model’s adaptation to unseen weather conditions without updating, by using few samples to recalibrate class prototypes, with samples classified based on a metric learning approach. Extensive experiments conducted on a real-world FOPEN dataset collected under multiple weather conditions demonstrate that the proposed method consistently outperforms two other selected metric-based meta-learning methods. More importantly, PNFOPENet maintains high recognition accuracy under cross-weather settings without fine-tuning, validating its effectiveness for few-shot FOPEN target recognition.