Fast and Accurate Indoor Localization Based on Spatially Hierarchical Classification

Location fingerprinting is a common approach to indoor localization. For good accuracy, the training set of sample fingerprints should be sufficiently large to be well-representative of the environment in terms of both spatial coverage and temporal coverage. As such, the computation required during the positioning phase can be expensive because we have to evaluate each new fingerprint against the training data repeatedly over time. It is desirable, therefore, to optimize computational efficiency, not just localization accuracy. Existing techniques are far from this goal due to their polarization toward one criterion but not both. We propose a substantially better technique based on the novel approach of modeling indoor localization as a classification learning problem where classes form a spatial hierarchy. Its performance is substantiated in our evaluation study.