A heading error estimation approach based on improved Quasi-static magnetic Field detection
The Zero-velocity Update (ZUPT)-aided Extended Kalman Filter(EKF) algorithm is commonly used to suppress the error growth of the inertial based pedestrian navigation systems, but it still suffers from long-term heading drift. The magnetic field was suggested to mitigate the heading errors of for positioning and navigation, but it undergoes severe perturbation in indoor scenarios. The Quasi-Static magnetic Field (QSF) method was developed to estimate heading errors using magnetic field in perturbed environments. However, this method may bring extra errors to system because of the high false alarm probability of detecting the quasi-static field. In this paper, we propose a heading error estimation approach based on the improved QSF approach for foot-mounted Inertial Navigation Systems (INS). For the approach, a magnetometer calibration method is developed to eliminate the deviation caused by the positioning system platform and shoes firstly. Then an improved QSF detection approach is proposed and then used for generating the desired magnetic measurements which are fed into the EKF to estimate the heading errors. The experiments indicate that the proposed method is effective in suppressing the heading errors.