Coriolis-Based Heading Estimation for Pedestrian Inertial Localization Based on MEMS MIMU

The zero-velocity-update (ZUPT)-Aided foot-mounted pedestrian inertial navigation system (PINS) is a powerful, high-precision, and autonomous positioning system for the IOT applications, such as pedestrian indoor and outdoor seamless positioning. The ZUPT-Aided PINS always suffers from the heading error, which leads to a high-order divergence rate of the positioning result. This study uses a magnetometer and proposes a Coriolis-based heading estimation (CHE) method to address this challenge. While the magnetometer is capable of directly correcting heading information, it is susceptible to environmental magnetic interference (MI). Based on the Coriolis theory, the CHE method ingeniously leverages angular rate and magnetic measurement to realize the decoupling between the effective magnetic information and MI. Furthermore, the pedestrians need high-precision height estimation, when walking between the multiple floors. This study proposes a height polynomial model based on linear transformation based on the barometer. The proposed method suppresses the long-Term drift of air-pressure measurement and improves the height-estimation precision. The above models are integrated into the ZUPT-Aided PINS. A linear Kalman filter is designed to fuse the information and suppress the errors of heading and height. At a complex-walking scenes, the experimental result shows that the proposed algorithm achieves higher 3-D average positioning precision. It is 2.897 m (0.145% mileage) under 30-min two-kilometer walking.