A Moving Horizon Estimator Performance Bound

Nicholas R. Gans () and Jess W. Curtis ()
Additional contact information
Nicholas R. Gans: National Research Council and Air Force Research Laboratory
Jess W. Curtis: Air Force Research Laboratory

Chapter Chapter 17 in Dynamics of Information Systems, 2010, pp 323-334 from Springer

Abstract: Summary 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.

Keywords: Kalman Filter; Unmanned Aerial Vehicle; Monte Carlo Analysis; Kalman Gain; Sigma Point (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4419-5689-7_17

Ordering information: This item can be ordered from
http://www.springer.com/9781441956897

DOI: 10.1007/978-1-4419-5689-7_17

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().