Residual-Based RKF with Recursive Measurement Noise Covariance Matching

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.