Fault Tolerant Estimation of UAV Dynamics via Robust Adaptive Kalman Filter

Abstract

A covariance scaling based robust adaptive Kalman filter (RAKF) algorithm is developed for the case of sensor/actuator faults. The proposed RAKF uses variable scale factors for scaling the process and measurement noise covariances and eliminating the effect of the faults on the estimation procedure. At first, the existing covariance estimation based adaptation techniques are reviewed. Then the covariance scaling methods with single and multiple factors are discussed. After choosing the efficient adaptation method an overall concept for the RAKF is proposed. In this concept, the filter initially isolates the fault, either in the sensors or in the actuators, and then it applies the required adaptation process such that the estimation characteristic is not deteriorated. The performance of the proposed filters is investigated via simulations for the UAV state estimation problem. The results of the presented algorithms are compared for different types of sensor/actuator faults and recommendations about their application are given within this scope.