A Moving Horizon Estimator Performance Bound
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.
KeywordsKalman Filter Unmanned Aerial Vehicle Monte Carlo Analysis Kalman Gain Sigma Point
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- 2.Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions, 3rd edn. Wiley, New York (1996) Google Scholar
- 6.Plett, G.L., Zarzhitsky, D., Pack, D.: Out of order sigma point Kalman filtering for target localization using cooperating unmanned aerial vehicles. In: Advances in Cooperative Control and Optimization, pp. 21–43 (2007) Google Scholar