Smartphone based Indoor tracking using magnetic and indoor maps

Tracking target user's indoor location with sub meter accuracy using low cost or no infrastructure is an active research topic. Indoor localization uses short range signals like WiFi, radio, ultrasound or Bluetooth signals that are affected by multipath errors and human presence. Recent studies have used stable magnetic field maps along with inertial sensors for indoor localization. In this paper, we propose a method that uses inertial sensors, magnetic field maps and indoor maps in a particle filter based implementation to improve accuracy of localization and tracking. Our method uses gradient descent algorithm to correct inaccurate user heading direction estimates due to magnetic perturbations. The tracking performance of our method is tested for four different implementations of Particle filter algorithm using magnetic maps (magnitude or vector) with and without indoor maps. We have developed an Android application to implement these four approaches and performed several experiments in a test area. The best approach out of these four achieves a mean localization accuracy of 0.75 m with a standard deviation of 0.52 m.