Towards area classification for large-scale fingerprint-based system

Abstract

In spacious and multi-area buildings, fingerprint-based localization often suffers from expensive location search. Besides, context knowledge like inside/outside-region and floor area is important for complete location service. To address above issues, beyond the algorithms finding the exact location point, we study accurate and efficient indoor area classification for large-scale fingerprint-based system. We first study leveraging the one-class classification to conduct inside/outside-region detection given only the inside fingerprints. Then we discuss different area determination algorithms, and compare their detection accuracy and deployment efficiency. To further enhance accuracy, we also discuss rejecting unclassifiable signals and calibrating heterogeneous devices. We have implemented different algorithms on Android platforms. Experimental trials (totally over 30,000 fingerprints and 15,000 test data) at an international airport, a business building, a premium shopping mall and a university campus have evaluated practicability and deployability of different classification schemes. Our studies can also serve as design guidelines for area classification.