University of New Orleans Fund
Advanced computing and sensing capabilities of smartphones provide new opportunities for personal indoor positioning. A particular trend is to employ human activity recognition for autonomous calibration of pedestrian dead reckoning systems thereby achieving accurate indoor positioning even in the absence of any positioning infrastructure. The basic idea is that the activity context, such as switching from a walking to a stair climbing activity gives clues about pedestrian's current position. In this paper, the authors have made a first attempt in developing a performance model for such systems. For an unbiased random walk, they have obtained two interesting results in closed-form expressions.