Meta-Learning Based Indoor Localization: A Hierarchical Framework

Machine learning (ML) has been considered a promising approach for indoor localization. Nevertheless, existing ML approaches require a large amount of computational resources and data samples in the new scenarios or for different types of mobile devices (MD). To address the issues, we first propose a hierarchical framework that divides the deep neural network (DNN) for localization into two parts and deploys them on the server and MD, respectively. The on-device part can estimate a coarse location of the MD with local information, and the other part can improve the accuracy with global information on the server. Then, we develop a meta-learning-based training algorithm that trains the DNN in different scenarios or with different types of MDs. Then, it is implemented in unseen scenarios or on new types of MDs, where a small number of data samples are available for fine-tuning the DNN. By implementing a learning-based localization algorithm in the hierarchical framework, the root mean square error (RMSE) achieved by the on-device DNN is 1.18 m and the inference time is less than 20 ms. With the help of global information at the server, the RMSE is reduced to 0.6 m.