Cooperative Localization in GPS-Limited Urban Environments

Existing localization techniques such as GPS have fundamental limitations which preclude deployment in urban canyons or areas with inconsistent network availability. Augmenting GPS requires specialized infrastructure or tedious calibration tasks which limit general purpose applications. In this paper, we examine the utility of cooperatively sharing location data among connected nodes in order to correct positions with high measurement error in GPS-limited environments. Using simple data sharing and filtering techniques, collaborating users can substantially reduce overall localization error in dead reckoning systems where nodes may have a broad spectrum of location quality. We examine system parameters necessary to fully exploit cooperative localization based on empirical error models and show that mean position error can be reduced by up to 50 percent for given application scenarios. If distance measurement is available, filtering location information based on estimated error and confidence can improve accuracy of pedestrian dead reckoning techniques to approximately that of GPS using trilateration.