Gnome: A Practical Approach to NLOS Mitigation for GPS Positioning in Smartphones

Accurate positioning in urban areas is important for personal navigation, geolocation apps, and ride-sharing. Smartphones localize themselves using GPS position estimates, and augment these with a variety of techniques including dead reckoning, map matching, and WiFi localization. However, GPS signals suffer significant impairment in urban canyons because of limited line-of-sight to satellites and signal reflections. In this paper, we focus on scalable and deployable techniques to reduce the impact of one specific impairment: reflected GPS signals from non-line-of-sight (NLOS) satellites. Specifically, we show how, using publicly available street-level imagery and off-the-shelf computer vision techniques, we can estimate the path inflation incurred by (the extra distance traveled by) a reflected signal from a satellite. Using these path inflation estimates we develop techniques to estimate the most likely actual position given a set of satellite readings at some position. Finally, we develop optimizations for fast position estimation on modern smartphones. Using extensive experiments in the downtown area of several large cities, we find that our techniques can reduce positioning error by up to 55% on average.