Handling spuriosity in the Kalman filter

Dennis K.J. Lin, Irwin Guttman

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)259-268
Number of pages10
JournalStatistics and Probability Letters
Issue number4
StatePublished - Mar 16 1993

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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