Dynamic Indoor Fingerprinting Localization based on Few-Shot
  Meta-Learning with CSI Images
Jiyu Jiao Xiaojun Wang Chenpei Han Yuhua Huang Yizhuo Zhang
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.