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