A yaw correction method for pedestrian positioning using two low-cost MIMUs

In pedestrian inertial navigation system, the traditional zero-velocity update (ZUPT) method establishes a velocity constraint. Based on the Kalman filter algorithm, the velocity error is utilized to correct the position drift. However, the yaw error cannot be observed. In this study, two low-cost magnetometers/inertial measurement units (MIMUs) are tied to the foot and shank, respectively. To improve the positioning accuracy, two error correction methods are developed. Firstly, the yaw angle coordinate is chosen as a Kalman filter measurement vector. Secondly, a geometric constraint is proposed. It includes degree of freedom constraint, angle constraint, and position vector constraint. Finally, different indoor and outdoor experiments are employed to verify the effectiveness of the proposed method. The average distance error percentage of the proposed method is less than 2%, and the heading bias is less than 5° within 5.05 min. Thus, the estimated paths are more in line with the actual trajectories.