WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 23, 2024
Residual-Based RKF with Recursive Measurement Noise Covariance Matching
Authors: ,
Abstract: A new residual-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed residual-based RKF with recursive estimation of measurement noise covariance is applied to the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real-time in the presence of various types of sensor faults. Estimation accuracies of the proposed RKF and conventional KF are investigated and compared.
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Keywords: Kalman filter, residual, robust estimation, unmanned aerial vehicle, sensor faults, covariance matching
Pages: 435-443
DOI: 10.37394/23202.2024.23.45