James-Stein state space filter

Jonathan H. Manton, Vikram Krishnamurthy, H. Vincent Poor

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In 1961, James and Stein discovered a remarkable estimator which dominates the maximum-likelihood estimate of the mean of a p-variate normal distribution, provided the dimension p is greater than two. This paper, by applying `James-Stein estimation theory', derives the James-Stein state filter (JSSF), which is a robust version of the Kalman filter. The JSSF is designed for situations where the parameters of the state-space evolution model are not known with any certainty.

Original languageEnglish (US)
Pages (from-to)3454-3459
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: Dec 10 1997Dec 12 1997

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Control and Systems Engineering
  • Modeling and Simulation


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