Research on the improved method for dual foot-mounted Inertial/Magnetometer pedestrian positioning based on adaptive inequality constraints Kalman Filter algorithm

摘要

The advantages of foot-mounted inertial pedestrian position system are passive, full-autonomous and not affected by environment. However, the disadvantage of the accumulating pedestrian position error, which is introduced by Micro Inertial Measurement Unit (MIMU) noise, limits its application. Zero Velocity Update (ZUPT) algorithm is proposed to correct the accumulating error. But ZUPT has poor performance due to the unobserved position error and azimuth misalignment angle, which results in a continuous increase in azimuth error and an increase in positioning error. To solve the problem, the improved method for dual foot-mounted Inertial/Magnetometer pedestrian positioning based on adaptive inequality constraints Kalman Filter is proposed in this paper. Our first contribution in this paper is studying the adaptive inequality constraints Kalman Filter algorithm for dual-foot inertial position algorithm. In this method, the adaptive inequality constraint is introduced in the Kalman Filter of ZUPT, and the constraints parameter can be online adaptive adjusted based on the character of pedestrian step length. Therefore, the accumulating position error can be decreased and limited in a range. As our second contribution, we propose an approach for estimating the magnetic disturbance in real time during the pedestrian walking period. After studying the observability properties of magnetic disturbance parameters, the magnetic disturbance can be estimated when the foot satisfied the conditions of static and angular motion. Then, the azimuth misalignment angle can be corrected by magnetometer during the ZUPT period. We illustrate the findings of our theoretical analysis using experiments based on the MTI-G710 MIMU. The achieved performance indicates that our proposed method can conveniently be used in consumer products. (C) 2018 Elsevier Ltd. All rights reserved.