Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images

Autor:	Jiao, Jiyu, Wang, Xiaojun, Han, Chenpei, Huang, Yuhua, Zhang, Yizhuo
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages,7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.05711 Zobrazit plný text záznamu View this record from Arxiv