MetaLoc: Learning to Learn Wireless Localization

The existing indoor fingerprinting localization methods are rather accurate after intensive offline calibration for a specific environment, no matter based on received signal strength (RSS) or channel state information (CSI), but the well-calibrated localization model (can be a pure statistical one or a data-driven one) will present poor generalization ability in the highly variable environments, which results in big loss in knowledge and human effort. To break the environment-specific localization bottleneck, we propose a new-fashioned data-driven fingerprinting method for localization based on model-agnostic meta-learning (MAML), named by MetaLoc. Specifically, MetaLoc is char acterized by rapldly adapting itself to a new, possibly unseen environment with very little calibration. The underlying localization model is taken to be a deep neural network, and we train an optimal set of environment-specific meta-parameters by leveraging previous data collected from diverse well-calibrated indoor environments and the maximum mean discrepancy criterion. We further modify the loss function of vanilla MAML and propose a novel framework named as MAML-DG, which is able to achieve faster convergence and better adaptation abilities by forcing the loss on different training domains to decrease in similar directions. Experiments from simulation and site survey confirm that the meta-parameters obtained for MetaLoc achieves very rapid adaptation to new environments, competitive localization accuracy, and high resistance to significantly reduced reference points (RPs), saving a lot of calibration effort.