HYFI: Hybrid Floor Identification Based on Wireless Fingerprinting and Barometric Pressure

Abstract

Identifying different floors in multistory buildings is a very important task for precise indoor localization in industrial and commercial applications. The accuracy from existing studies is rather low, especially in multistory buildings with irregular structures such as hollow areas, which is common in various industrial and commercial sites. As a better solution, this paper proposes a hybrid floor identification (HYFI) algorithm, which exploits wireless access point (AP) distribution and barometric pressure information. It first extracts the distribution probability of APs scanned in different floors from offline training fingerprints and adopts Bayesian classification to accurately identify floor in well-partitioned zones without hollow areas. The floor information obtained from wireless AP distribution is then used to initialize and calibrate barometric pressure-based floor identification to compensate variable environmental effects. Extensive experiments confirm that the HYFI approach significantly outperforms purely wireless fingerprinting-based or purely barometric pressure-based floor identification approaches. In our field tests in multistory facilities with irregular hollow areas, it can identify the floor level with more than 96.1% accuracy.