State Estimation and Fault Detection of an Aircraft Using Nonlinear Filters
Julia Christmann, Carsten Kappenberger, Nicole Marheineke · PAMM · 2011
Abstract An aircraft is a complex technical system, which has to satisfy high safety standards. The whole system of an aircraft has to be monitored because more and more actions happen automatically. Therefore unpredictable errors should be detected as fast as possible. In this work two different nonlinear filters namely the Extended Kalman Filter (EKF) and the Particle Filter (PF) are studied for the application of fault detection with simultaneous estimations of states. Results show that EKF leads to better approximation for nearly linear problems, while PF yields a more accurate approximation for worst case scenarios. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)