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
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Citations: View citations in EconPapers (3)

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