Probabilistic-Adaptive Kalman Filtering for Fault-Tolerant Estimation of UAV States

A probabilistic-adaptive Kalman filter (pAKF) algorithm for the fault tolerant estimation of Unmanned Aerial Vehicles (UAV) dynamics in the presence of measurement faults is proposed. The proposed pAKF based on the evaluation of the posterior probability of the normal operation of the system, given for the current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behaviour. The developed pAKF algorithm is applied for the fault tolerant estimation of the UAV dynamics. The proposed pAKF algorithm is tested for the two different measurement malfunction scenarios; continuous bias at measurements and measurement noise increment.