Indoor Geomagnetic Positioning Using Direction-Aware Multiscale Recurrent Neural Networks

Accurate indoor positioning has become an indispensable technology for location-based service in indoor environments. Geomagnetic field fingerprinting for indoor positioning is an attractive alternative to Wi-Fi and Bluetooth because of the omnipresent signals and the infrastructure-free mode. However, heterogeneous devices and users with fluctuant geomagnetism and massive crowdsourced data covering large-scale indoor scenes may result in a low degree of discernibility and degrade the rerecognizable performance for pedestrian positioning. To overcome the problem, a deep-learning-based indoor geomagnetic positioning method with direction-aware multiscale recurrent neural networks (DM-RNNs) is presented. First, the direction information is taken into the fingerprint construction and localization process to increase spatial identification, which is necessary for indoor pedestrian navigation. Second, instead of using a single holistic feature from sequence fingerprints directly, we employ the different scale-based feature extraction units for variational anomalies of the signal by using multiscale RNNs, increasing the model adaptability and generality for random pedestrian motion and device heterogeneity. Third, the ensemble learning mechanism is adopted in the model to obtain robust localization results. Furthermore, we employ the convenient visual-inertial odometry (VIO) to collect the geomagnetic signal dataset and utilize the sequence augmentation approach to generate synthesized trajectories from multiple single-point magnetic values for training and testing the model. Extensive experiments in three different indoor scenes demonstrate that the proposed approach outperforms state-of-the-art competing schemes by a wide margin, reducing mean localization error by more than 30% and obtaining over 79% direction estimation precision in different indoor scenarios.