Evaluation of AHRS algorithms for Foot-Mounted Inertial-based Indoor Navigation Systems

Abstract

Personal Dead Reckoning based on foot-mounted Inertial Measurement Units is a research hotspot in the field of positioning and navigation in recent years. This paper conducts a targeted research on the application of current mainstream attitude and heading reference system (AHRS) algorithm in the foot inertial navigation positioning. Through open datasets, the positioning accuracy and directional accuracy of 9-state complementary Kalman filter (CKF) are compared and analyzed among the conventional algorithm, Mahony algorithm, and Madgwick algorithm, in which the Madgwick algorithm can achieve the best positioning results. And on this basis, for the Madgwick algorithm, it is verified that it can help improve the positioning accuracy of 15-state CKF under the assistive technologies of zero angular rate update (ZARU) and heuristic heading reduction (HDR). The adaptive zerospeed detection algorithm is designed, and the threshold value of zero-speed detection is set dynamically through tracking the variable of speed in CKF, which can detect the time period of zero-speed state more accurately, thus further improving the correction of directional errors. Finally, the effectiveness of the proposed algorithm is further proved by actual data.