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 triaxis accelerometers, triaxis gyroscopes, triaxis 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 the constraints to upgrade the performance of the multisensor enhanced PDR. Results based on a set of experimental data demonstrated that the proposed PDR could provide positioning accuracy in terms of the root mean-square error (RMSE) within 1.5 m. Compared to the PDR-only, the position enhancements on vertical and horizontal from a barometer, triaxis accelerometers, triaxis 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.