Evaluating mismatch probability of activity-based map matching in indoor positioning

If users are known to perform specific activities at specific locations within a building, then indoor positioning could be achieved by monitoring user activities and matching them to specific locations in a preloaded floor map. This is the fundamental idea behind activity-based map matching (AMM). For example, the user´s smartphone could use the accelerometer readings to detect whether a user is using an escalator, and then match the current location of the user to the nearest escalator. AMM therefore could be used for frequently recalibrating location estimators to ground-truth values. This is especially useful for recalibrating pedestrian dead reckoning (PDR), which can estimate indoor position if started from a known location, but error grows unboundedly with time or distance traveled. However, AMM is not perfect and could potentially cause mismatches by matching the current location of the user to a wrong location. In this paper we propose a methodology and derive a closed-form expression for mismatch probability as a function of PDR sensor error and proximity between two facilities. By applying our methodology to a practical indoor complex (Sydney airport) we find several interesting results: (1) that mismatch probability is spatially non-uniform, i.e., it can be different in different parts of the floor, (2) for some specific facilities, mismatch probability can be very high (up to 80%), and (3) if escalators could be distinguished from lifts with high accuracy, we could reduce mismatch probability significantly (by up to 68%).