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Optimal Reconstruction of Flight Simulator Motion Cues using Extended Kalman Filtering


Pool,  DM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Pool, D., Chu QP, Mulder, M., & van Paassen, M. (2008). Optimal Reconstruction of Flight Simulator Motion Cues using Extended Kalman Filtering. In AIAA Modeling and Simulation Technologies Conference 2008 (pp. 495-509). Red Hook, NY, USA: Curran.

For evaluation of simulator motion and motion platform dynamics, the motion cues generated in flight simulators need to be measured. For the SIMONA Research Simulator at TU Delft, the availability of redundant kinematic motion sensors i.e. an Inertial Measurement Unit and sensors that measure the lengths of the motion system actuators was expected to allow for optimal estimation of the flight simulator motion state using an Extended Kalman Filter. As a starting point, this sensor fusion problem was evaluated for only symmetrical simulator motion, omitting the additional asymmetrical motion states. The highly nonlinear relation between the extension of the motion base actuators and simulator position and orientation was found to require the application of an Iterative Extended Kalman Filter to ensure adequate filter convergence. Using this iterative filter, optimal estimates of the symmetrical simulator state and the IMU biases could be obtained from the two sets of redundant kinematic observations.