A Novel Pedestrian Dead-Reckoning Algorithm With Self-Estimates of IMU Biases

To suppress the navigation error of the foot-mounted pedestrian dead reckoning (PDR) system, many works, for example, zero velocity-update (ZUPT)-based methods, have been done without the assistance of the external sensors, but they all only can work well in the stance phase. To further improve the performance of the PDR system in the swing phase, a novel PDR method with self-estimates of inertial measurement unit (IMU) biases is proposed in this article. Differing from the existing methods in PDR, the bias of the accelerometer in the body frame, rather than the navigation frame, is assumed to be constant. Then, a novel perspective to self-estimate the bias of the accelerometer in the body frame is provided, which is determined by the attitude matrix decomposition method. Finally, the attitude angles, the velocities, and the position results during the swing phase will be recalibrated by feedbacking the self-estimated biases. Field tests illustrate that the proposed method has a better performance than that of the existing state-of-the-art algorithms.