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.

翻译：虽然指纹定位因其有效性备受青睐，但高昂的数据采集成本与基于静态数据库估计的不准确性制约了其应用。针对这些问题，本文提出一种创新的室内定位方法，采用数据高效的元学习算法。该方法基于元学习的"学会学习"范式，利用历史定位任务提升动态室内环境中的适应性与学习效率。我们引入任务加权损失函数以增强该框架内的知识迁移能力。综合实验验证了该方法的鲁棒性及其对现有基准的优越性，在平均欧氏距离上实现了显著的23.13%性能提升，尤其适用于信道状态信息数据有限的场景。