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 uctures 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 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 well zones without hollow areas. The floor iinformation nformation obtained from wireless AP distribution is then used to initialize and calibrate barometric pressure pressure-based floor identification to compensate variable environmental effects. Extensive experiments confirm that the HYFI approach significantly outpe outperforms purely wireless fingerprinting-based based or purely barometric pressure 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.