PUBLISHED PAPERS #4.03
| Chingiz Hajiyev, Ulviye Hacizade Adaptive Kalman Filter with Recursive System Noise Covariance Estimation Applied to UAV Dynamics |
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| Abstract. A novel covariance difference-based system noise covariance estimation method for Kalman filter tuning is proposed. Q- adaptive Kalman filter with recursive system noise covariance estimation is derived. Influence of the system noise bias to the state correction sequence of Kalman filter (KF) is investigated. It is shown in the study that the bias type system 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. The proposed adaptive adjustment of system noise covariance in Kalman filter is applied for the model of unmanned aerial vehicle (UAV) dynamics. Algorithm is tested for different system noise bias scenarios. The simulation results show that the proposed covariance difference-approach based adaptive Kalman filter (AKF) with recursive Q-adaptation can estimate the UAV dynamics accurately in real time in the presence of system noise uncertainties. The estimation accuracies of the adaptive and non-adaptive versions of the Kalman filter are compared in the presence of system noise bias type changes. |
| Keywords: kalman filter, state correction sequence, adaptive estimation, unmanned aerial vehicle, system noise, covariance estimation |
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