Robust Estimation of UAV Dynamics in the Presence of Measurement Faults

Abstract

This study introduces a robust Kalman filter (RKF) with a filter-gain correction for cases of measurement malfunctions. Using defined variables called measurement-noise scale factors, the faulty measurements are taken into consideration with a small weight and the estimations are corrected without affecting the characteristics of the accurate ones. In this study, RKF algorithms with single and multiple scale factors are proposed and applied for the state estimation process of an unmanned aerial vehicle (UAV) platform. The results of these algorithms are compared for different types of measurement faults, and recommendations for their utilization are given.