Q-adaptive AKF for Estimation of UAV dynamics in the Case of Biased Process Noise

In this study a covariance matching-based adaptive Kalman filter (AKF) with Q-adaptation is examined. Influence of the process noise bias type system changes to the prediction, estimation and state correction sequence of Kalman filter (KF) is investigated. It is proved that the bias type process noise change may be converted to the mean square of state correction sequence of KF and such type of changes can be compensatd using the covarince matching techniques.