Improvement of Kalman Filter for GNSS/IMU Data Fusion with Measurement Bias Compensation

Abstract

The ASEAN IVO project currently supports the research related to GNSS and ionospheric data products for disaster prevention and aviation in low-latitude regions. In vehicle navigation, Real Time Kinematic (RTK) positioning distorted from the environment often contaminates the measurement vectors (such as position or speed of a rover). In this situation, the conventional Kalman filter with a linear motion model could not reduce positioning errors sufficiently due to existing bias. Hence, the measurement bias compensation method based on the mean of residual vectors is proposed. We modify the conventional Kalman filter by including this compensation in the estimation step. We test the algorithm in both of the simulations and actual experiments. From the results, the proposed method outperforms the baseline method in terms of positioning error by 60% and 12% for the simulation test and the field test respectively.