An Adaptive Heading Correction Algorithm for Suppressing Magnetic Interference in Inertial Navigation System

The pedestrian navigation system using inertial and magnetic sensors can determine the pedestrian's attitude and position. However, magnetic field measurement is affected by external magnetic interference, which leads to heading error. Therefore, it is of great importance to suppress magnetic interference. The magnetic threshold method is often used to detect magnetic interference. However, this method fails under the weak magnetic interference environment because the magnetic field intensity measurement is approximately equal to that of the geomagnetic intensity. A generalized likelihood ratio test (GLRT) is proposed in this study. A likelihood ratio function is constructed to maximize the phase probability of magnetic interference so that the inequality relation for detection can be determined. In addition, zero velocity update (ZUPT) algorithm including extended Kalman filter (EKF) or extended Kalman particle filter (EKPF) can suppress accumulated errors, but the heading deviation cannot be accurately estimated. Therefore, an improved EKPF is designed to compensate heading observation by adding adaptive parameters. In order to verify the advantages of the proposed method, the pedestrian navigation system is established and experiments are performed. The false detection rate of the proposed GLRT method decreased by 14.36% compared with the conventional method. Furthermore, the positioning error is reduced by improved EKPF, compared with EKF and EKPF. Therefore, the methods proposed in this study improve the heading and positioning accuracy under a magnetic interference environment.

Automated summary

The pedestrian navigation system using inertial and magnetic sensors can determine the pedestrian's attitude and position, but magnetic interference can lead to heading errors. This study proposes the GLRT method to suppress magnetic interference and improve the accuracy of the navigation system.