Smartphone based indoor tracking using magnetic and indoor maps

Abstract

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.

Indoor Multi-Floor 3D Target Tracking Based on the Multi-Sensor Fusion

“…The second method is map-matching algorithm correction, such as indoor plan map and magnetic map. Putta et al [13] uses gradient descent algorithm (GDA) and map to correct the heading drift caused by magnetic field perturbations. This method does not rely on indoor equipment, which is low cost and high localization accuracy.…”

“…In indoor environment, the air pressure value is easily affected by the characteristics of meteorological environment and the sensor's limited accuracy, As shown in the in Fig. 3, the maximum pressure fluctuation value of the built-in barometer of smartphone on the same floor is up to 27Pa, which is 2.267m by using (13).…”

“…(2) Compared with the sensor-based 2D plane trajectory scheme in [13]- [15], [31], [32], we expand the 2D trajectory tracking of pedestrian indoor to 3D space successfully; Compared with the 3D trajectory in [1], [22], our experimental path is more complex, and the error analysis of each step of the trajectory is carried out. The complex experimental path introduces more challenges to our algorithms, but the result is more reliable and robust.…”

“…Therefore, the inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) system has become more accurate and easier to implement. In recent years, there is increasingly researches have focused on smartphone-based indoor pedestrian tracking filed [1], [13]. To obtain the position of the pedestrian, the PDR algorithm uses the smartphone's built-in inertial sensors on the basis of the kinematic characteristics of pedestrians to estimate the step length and walking direction at each detected step [14], [15].…”

Indoor Positioning Based on Pedestrian Dead Reckoning and Magnetic Field Matching for Smartphones

“…Grand et al [16] presents an online particle filter that achieved localization accuracy of 0.7 m in a square room. Putta et al [24] uses gradient descent algorithm to improve users’ heading estimation, and utilizes magnetic field maps and indoor maps to improve the particles weight estimation. Test results show that the system achieves a mean localization accuracy of 0.75 m. Kim et al [25,26] propose magnetic gradient sequence based technique to mitigate the influence of the bias of magnetometer, which has much more adaptable to different user devices than the conventional PF.…”