GeoLoc: A Geomagnetic Indoor Localization Algorithm with Iterative Uncertainty Elimination

Geomagnetic field signal has gained increasing wide investigated for indoor positioning problems. Because of the variation of magnetic signals and the sensor observation drift, the recent positioning technology development based on magnetic field or pedestrian dead reckoning (PDR) has been restricted. In addition, the accumulative error and cold-start problem can also cause huge positioning error. In this paper, we present a novel indoor localization approach, GeoLoc, for combining magnetic fingerprint matching and PDR by Kalman Filter. First, magnetic field intensity of every positions is gathered and a fingerprint map is built for matching. A candidate set of positions is introduced to include uncertainty and increase robustness for our estimation. With the squeezing of candidate sets, uncertainties of orientation and position estimation have been eliminated. Realistic experiment results show that GeoLoc successfully addresses accumulative error and cold-start problems by sensor data fusion. GeoLoc achieves a good estimation for both short (less than 17.5m) and long walking distance, and it can work in both offline and online real-time positioning. GeoLoc is able to achieve an online positioning accuracy of less than 1.2m, and an offline positioning accuracy of 0.3m only with a smart phone. GeoLoc only uses the built-in sensors of mobile phones, thus users can get their position only by using their phone.