1. Department of Precision Instrument, Tsinghua University, Beijing 100084, China;
2. Department of Control Engineering, Academy of Armored Force Engineering, Beijing 100072, China
Abstract：Foot-mounted inertial navigation systems (INS) having inexpensive micro electro mechanical system (MEMS)-based inertial sensors are widely used for pedestrian navigation. A foot-mounted INS can be combined with a magnetometer to constrain the heading angle error, but the magnetometer needs to be calibrated before use. This paper presents a magnetometer error model and an online calibration algorithm based on the foot-mounted INS characteristics. The magnetometer error characteristics and the foot-mounted INS mobility characteristics are used to develop a state equation and a magnetometer error measurement equation. An extended Kalman filter (EKF) is used for the online estimation and real-time calibration of the three-axis magnetometer errors with the zero velocity update (ZUPT) algorithm and a magnetic heading angle constraint algorithm for error constraint. The algorithm is validated by walking in a square playing ground. The results show that the online estimation and calibration algorithm reduce the end position error of the east direction from -110.7 to 1.8 m and the end position error of the north direction from 37.8 to 5.2 m compared to the system without calibration. This algorithm provides online calibration of magnetometer errors and significantly improves the positioning accuracy of pedestrian navigation.
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2016, Vol. 56 
