Reliable Positioning Model of Smartphone Sensors and User Motions Tightly Enhanced PDR

Smartphone-based Pedestrian Dead Reckoning (PDR) is widely used in Internet of Things (IoT) applications. However, the accuracy and reliability of PDR could be affected by the users’ environments significantly. To upgrade PDR’s performance, we present an enhanced PDR algorithm, in which pedestrian motion constraints, smartphone sensors, and a combined step detection method are integrated with PDR to provide continuous, accurate, and reliable position solutions. In such a method, measurements of tri-axis accelerometers, tri-axis gyroscopes, tri-axis magnetometers, a barometer, and a Global Navigation Satellite System (GNSS) chip from HUAWEI Mate30Pro are integrated by an Extended Kalman Filter (EKF). Pedestrian motions like motionless and linear motion form constraints to upgrade the performance of the multi-sensor enhanced PDR. Results based on a set of experimental data demonstrated that the proposed PDR could provide positioning accuracy in terms of Root Mean Square Error (RMSE) within 1.5 m. Compared to PDR-only, the position enhancements on vertical and horizontal from a barometer, tri-axis accelerometers, tri-axis magnetometers, GNSS, and motion constraints are visible with improvement percentages of more than 98.9%, which makes the position solutions of the presented PDR much more reliable than the conventional PDR.