3D indoor positioning with pedestrian dead reckoning and activity recognition based on Bayes filtering

Since state-of-the-art smartphones do usually not comprise barometers, ubiquitous 3D indoor positioning requires a compensation of the missing height information. A pedestrian activity classification (PAC) algorithm enabling the activity detection of going up- or downstairs can deliver this missing information. Additionally, this PAC can be used for the support of pedestrian dead reckoning (PDR) algorithms. An efficient PAC assists PDR algorithms by using activity information for the reduction of errors within step length estimation. Within this paper, a PAC based on inertial smartphone measurements followed by a stair detection to constrain floor changes in the multi-level filtering process is illustrated. The output of the PAC, the absolute WLAN positioning, as well as the PDR algorithm are filtered within a particle filter and presented within this paper.