A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.