Abstract
A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, barometer, or magnetometer, which enable the measurement of the step length and the heading direction. In this plan, a method based on IMU/EKF+HMM+ZUPT+ZARU+HDR+the Earth Magnetic Yaw was designed to realize foot-mounted pedestrian navigation. Based on the characteristics of pedestrian navigation, the general likelihood ratio test (GLRT) and the Hidden Markov Model (HMM) were used to realize the detection of zero speed interval at different speed states. When the zero speed state is detected, the zero velocity update (ZUPT) method is used to limit the accumulation of IMU. The Zero Angular Rate Update (ZARU) + (heuristic heading reduction) HDR+the Earth Magnetic Yaw method is used to limit the IMU attitude and heading drift. Finally, the EKF method is used to realize the effective estimation and feedback of the speed, attitude and heading error of the pedestrian navigation system. Meanwhile, a fault detection algorithm based on the innovation vector is added to the EKF system to effectively detect and eliminate the gross errors in the measurements, to improve the filtering effect of EKF algorithm, and ensure the accuracy of pedestrian navigation results.
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