Handling spuriosity in the Kalman filter
Dennis K. J. Lin and
Irwin Guttman
Statistics & Probability Letters, 1993, vol. 16, issue 4, 259-268
Abstract:
The Kalman filter, which is in popular use in various branches of engineering, is essentially a least squares procedure. One well-recognized concern in this least squares procedure is its non-robustness to spuriously generated observations that give rise to outlying observations, rendering the Kalman filter unstable, with devastating consequences in some situations. Much evidence exists that data almost always contain a small proportion of spuriously generated observations, and indeed, one wild observation can make the Kalman filter unstable. To handle this, we introduce a new recursive estimation scheme which is found to be robust to spurious observations. Examples are given to illustrate the new scheme.
Keywords: Kullback--Leibler; distances; mixture; distribution; robust; filter; spurious; observations (search for similar items in EconPapers)
Date: 1993
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(93)90129-7
Full text for ScienceDirect subscribers only
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:eee:stapro:v:16:y:1993:i:4:p:259-268
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul
More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().