An Improved Innovation Adaptive Kalman Filter for Integrated INS/GPS Navigation

Abstract

The performance of transportation systems has been greatly improved by the rapid development of connected and autonomous vehicles, of which high precision and reliable positioning is a key technology. An improved innovation adaptive Kalman filter (IAKF) is proposed to solve the vulnerability of Kalman filtering (KF) in challenging urban environments during integrated navigation. First, the algorithm uses the innovation to construct a chi-squared test to determine the abnormal measurement noise; on this basis, the update method of the measurement noise variance matrix is improved, and the measurement noise variance matrix is adaptively updated by the difference between the current innovation and the mean value of the innovation when the measurement data is abnormal so as to reflect the impact degree of the current abnormal measurement data, thus suppressing the filtering divergence and improving the positioning accuracy. The experimental results show that the proposed algorithm can well suppress the filtering divergence when the measurement data are disturbed. The results demonstrate that the algorithm in this paper has improved adaptiveness and stability and provides a novel idea for the development of an intelligent traffic positioning system.