Estimation of Unmanned Aerial Vehicle Dynamics in the Presence of Sensor Faults

In this chapter, a robust Kalman filter (RKF) with filter gain correction for the case of measurement malfunctions is introduced. By using a defined variable called the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristics of the accurate measurements. The RKF algorithms with single and multiple measurement noise scale factors (R-adaptation procedure) are proposed and applied for the state estimation process of the unmanned aerial vehicle (UAV) platform in the presence of measurement faults. The results of these algorithms are compared for different types of sensor faults and recommendations about their utilization are given. A remark on stability for the proposed RKFs is also included.

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