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.

利用一种数据高效的元学习算法，本文提出了一种创新的室内定位方法，解决了指纹定位在数据获取成本高和基于静态数据库估计的不准确性方面的问题。使用历史定位任务改善适应性和学习效率，在动态室内环境中采用 “学习如何学习” 的元学习范式。我们引入了任务加权损失函数来提高这一框架内的知识传递。全面的实验验证了该方法在有限 CSI 数据场景中的稳健性和优越性，平均欧几里得距离提升了 23.13%。