Referenced Items are not available for this document.
The minimum variance estimates of state variables in a noisy, nonlinear discrete-time system are evaluated by a Monte Carlo method. The a posteriori probability density function for state variables conditioned upon measurement data sequence is expanded into a series of orthonormal Hermite functions and numerically determined in a recursive form. The numerical results indicate that the proposed method can markedly improve the accuracy by using the quasi-random numbers.
Published in:
Automatic Control, IEEE Transactions on
(Volume:17
,
Issue:
5
)
Date of Publication: Oct 1972
- Page(s):
- 681 - 684
- ISSN :
- 0018-9286
- Digital Object Identifier :
- 10.1109/TAC.1972.1100095
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 January 2003
- Issue Date :
- Oct 1972
- Sponsored by :
- IEEE Control Systems Society
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