An Ellipsoidal Set-Membership Approach to Distributed Joint State and Sensor Fault Estimation of Autonomous Ground Vehicles

This paper is concerned with the problem of distributed joint state and sensor fault estimation for autonomous ground vehicles subject to unknown-but-bounded (UBB) external disturbance measurement noise. In order improve reliability performance in cases poor data collection potential communication interruption, a multi-sensor network configuration presented cooperatively measure vehicular yaw rate, further compute local estimates. Toward this aim, an augmented descriptor vehicle model first established, where unknown modeled as auxiliary system model. Then, new ellipsoidal set-membership approach developed so construct optimized bounding set which guarantees contain vehicle's true at each time step despite existence UBB noises. Furthermore, convex optimization algorithm put forward such that gain matrix estimator can be recursively obtained. Finally, simulation results are provided validate effectiveness proposed approach.