Innovation-based Adaptive Kalman Filter for INS/GPS

In this paper, the two main approaches of adaptive Kalman filtering, namely, innovation-based adaptive estimation (IAE) and multiple-model-based adaptive estimation (MMAE), are discussed for the case of INS/GPS integration. The development of an innovation-based adaptive Kalman filter, using the maximum likelihood criterion for an integrated INS/GPS system is then discussed in some detail. Results from two kinematic field tests in which the INS/GPS was compared to highly precise reference data are presented. Results show that the adaptive Kalman filter outperforms the conventional Kalman filter by tuning either the system noise variance-covariance (V-C) matrix ‘Q’ or the update measurement noise V-C matrix ‘R.’