A Moving Horizon Estimator Performance Bound

  • Nicholas R. Gans
  • Jess W. Curtis
Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)


Moving Horizon implementations of the Kalman Filter are widely used to overcome weaknesses of the Kalman Filter, or in problems when the Kalman Filter is not suitable. While these moving horizon approaches often perform well, it is of interest to encapsulate the loss in performance that comes when terms in the Kalman Filter are ignored. This paper introduces two methods to calculate a worst case performance bound on a Moving Horizon Kalman Filter.


Kalman Filter Unmanned Aerial Vehicle Monte Carlo Analysis Kalman Gain Sigma Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.National Research Council and Air Force Research LaboratoryEglin Air Force BaseUSA
  2. 2.Air Force Research LaboratoryEglin Air Force BaseUSA

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