A robust floor localization method using inertial and barometer measurements

Vertical height estimation is critical to indoor localization technique. However, the common story height covers from 2.8m to 6.0m in multistory buildings, which make it meaningless to estimate height alone. An efficient indoor location system should provide accurate floor estimation with fuzzy story height information. This paper proposes a Bayesian Network inference method to identify pedestrian's floor level accurately in a multistory building with a waist-mounted device. The algorithm adopts an effective activities detector of stair climbing at first. With the output of the detector, the landing is counted and the height change is calculated by barometer measurements. Finally, based on the landing number and height change value, a Bayesian Network model is introduced to infer the floor change of the pedestrian. The experiments reveal that the proposed floor localization algorithm is more reliable, which achieves an accuracy of 99.36% with a total number of 1247 times floor change.