|Abstract:||Pedestrian location in urban or indoor environments is particularly complex. Indeed, GNSS technology generally used for localization is no longer sufficient in these challenging environments. However, the presence of many sensors in consumer equipment like smartphones allows the implementation of different methods. PDR (Pedestrian Dead Reckoning) is a position estimation method using inertial and magnetic sensor data. It is based on the determination of two elements: the step length and the walking direction. This direction is difficult to estimate for handheld sensors because the orientation of the sensor is not always aligned with the walking direction. Methods based on the study of horizontal hand accelerations can overcome this difficulty, but performance on real scenarios is not sufficient. This article proposes a new method for estimating the walking direction and position based on an extended Kalman filter. For this purpose, the angular estimates from the WAISS and MAGYQ algorithms are merged to update the estimate of the walking direction. Phase measurements are used with TDCP updates to correct the velocity and correct the walking direction. 6 experiments carried out with three subjects over distances between 650 and 1300m in texting mode, in real and challenging conditions are conducted. The mean angular error obtained is 4.6° and the mean position error is 0.5% of the travelled distance.|
Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
|Pages:||367 - 377|
|Cite this article:||
Perul, Johan, Renaudin, Valérie, "Fusion of Attitude and Statistical Walking Direction Estimations with Time-Difference Carrier Phase Velocity Update for Pedestrian Dead Reckoning Method," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 367-377.
ION Members/Non-Members: 1 Download Credit