Near Real Time Heading Drift Correction for indoor pedestrian tracking based on sequence detection

摘要

Foot-mounted inertial sensor is a practical and efficient approach for indoor personnel positioning, because it requires neither pre-deployed infrastructure nor on-line “fingerprint” database. To suppress the heading drift of gyroscopes, magnetic field is often used as a stable orientation reference. But the magnetic field inside buildings changes rapidly while space and time changes, and it is also sensitive to some metal objects or electrical equipments. Thanks to the regular structures of most buildings, Heuristic Drift Elimination (HDE) techniques can be applied to suppress slowing-changed small-scale drifts between two steps, under the assumption that most of the paths in the buildings consist of straight lines and fixed angles. Inspired by the heuristic method, a Near Real-Time Heading Drift Correction (NRHDC) approach is proposed in this paper, which determines the current walking behavior based on the steps foregoing and following. Different step status are set to classify each step and different templates are trained for walking behavior discrimination. This approach is supposed to exploit more information and give more precise result. The experimental results in a building also demonstrate the effectiveness of the proposed approach, with superior accuracy to other methods.