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 letter presents an innovative indoor localization method using a data-efficient meta-learning algorithm. This approach, grounded in the ``Learning to Learn'' paradigm of meta-learning, utilizes historical localization tasks to improve adaptability and learning efficiency in dynamic indoor environments. We introduce a task-weighted loss to enhance knowledge transfer within this framework. Our comprehensive experiments confirm the method's robustness and superiority over current benchmarks, achieving a notable 23.13\% average gain in Mean Euclidean Distance, particularly effective in scenarios with limited CSI data.
@article{arxiv.2401.05711,
title = {Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images},
author = {Jiyu Jiao and Xiaojun Wang and Chenpei Han and Yuhua Huang and Yizhuo Zhang},
journal= {arXiv preprint arXiv:2401.05711},
year = {2024}
}