An Informational Approach for Fault Tolerant Data Fusion Applied to a UAV’s Attitude, Altitude, and Position Estimation

This paper presents a fault tolerance architecture for data fusion mechanisms that tolerates sensor faults in a multirotor Unmanned Aerial Vehicle (UAV). The developed approach is based on the traditional duplication/comparison method and is carried out via error detection and system recovery to both detect and isolate the faulty sensors. It is applied on an informational framework using extended Informational Kalman Filters (IKF) for state estimation with prediction models based on available sensors measurements. Error detection is realized through residuals comparisons using the Bhattacharyya Distance (BD), an informational measure that estimates the similarity of two probability distributions. An optimal thresholding based on Bhattacharyya criterion is applied. In order to identify the faulty sensor, the Bhattacharyya distance between the a priori and a posteriori distributions of each IKF is also computed. The system recovery is done by substituting the erroneous state by an error-free state. The proposed architecture alleviates the assumption of a fault-free prediction model using the information surprise concept instead of hardware redundancy.The performance of the proposed framework is shown through offline validation using real measurements from navigation sensors of a multirotor UAV with fault injection.