Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 3454-3459 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 4 |
| State | Published - 1997 |
| Externally published | Yes |
| Event | Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA Duration: Dec 10 1997 → Dec 12 1997 |
All Science Journal Classification (ASJC) codes
- Control and Optimization
- Control and Systems Engineering
- Modeling and Simulation