Few-Shot Meta-Learning for Dynamic Indoor Fingerprinting Localization Based on CSI Images
Jiyu Jiao, Xiaojun Wang, Chengpei Han, Yuhua Huang, Yizhuo Zhang · 2025
While fingerprinting localization is favored for its effectiveness, it is hindered by high data acquisition costs and the inaccuracy of static database-based estimates. Addressing these issues, this work presents an innovative dynamic indoor localization method using a few-shot meta-learning algorithm to optimize performance with limited CSI data. By leveraging the “Learning to Learn” paradigm, this method utilizes past localization tasks and requires only a few updates, enhancing adaptability and learning efficiency in new environments. Furthermore, we introduce a task-weighted loss to improve knowledge transfer within this framework. Experiments confirm the method's robustness and superiority, achieving a notable 25.08% average gain in Mean Euclidean Distance (MED), proving especially effective in scenarios with limited CSI data.