Handling spuriosity in the Kalman filter
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.
Year of publication: |
1993
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Authors: | Lin, Dennis K. J. ; Guttman, Irwin |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 16.1993, 4, p. 259-268
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Publisher: |
Elsevier |
Keywords: | Kullback-Leibler distances mixture distribution robust filter spurious observations |
Saved in:
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