PhaseFi: Phase Fingerprinting for Indoor Localization with a Deep Learning Approach

With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card (NIC) by accessing the modified driver. Then a linear transform is used to extract the calibrated phase information, which is proven to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ weights to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a sub-network between two continuous layers forms a Restricted Boltzmann Machine (RBM). In the online stage, we use a probabilistic method based on the radial basis function (RBF) for online location estimation. The proposed PhaseFi scheme is implemented and validated with intensive experiments in two representation indoor environments. It outperforms other three benchmark schemes based on CSI or RSS in both scenarios.