Indoor Localization with Spatial and Temporal Representations of Signal Sequences

Abstract

Indoor localization has attracted considerable attention lately, due to its large commercial and social values in smart cities. The existing indoor localization approaches mostly rely on fingerprint techniques, and many of those leverage either spatially discrete fingerprints or temporally consecutive ones for localization, which either suffers from large errors due to signal ambiguities or high time overhead with long sequences. To achieve high accuracy with low computational cost, we propose ST-Loc, a deep neural network that extracts features from multiple representations of a single signal sequence for localization, where each representation indicates a corresponding signal structure with underlying feature correlations. Taking geomagnetism as an example, we infer location features from two different representations, e.g., spatial and temporal. In spatial representation, a signal sequence is converted to a signal heatmap, where each pixel corresponds to a spatial location and the value indicates fingerprint. Temporal representation, on the other hand, is a signal sequence with ordered readings, which provides temporal correlations. Using these different representations, we employ convolutional and recurrent networks to extract location features and fuse them to generate more distinguishing features for localization. We have conducted extensive experiments in two different trial sites, a narrow office area and a spacious food plaza. Our experimental results show that ST- Loc achieves more than 43% average localization error reduction compared with state-of-the-art competing schemes in both trial sites.