Recursive Estimator for Linear and Nonlinear Systems with Uncertain Observations

Document Type

Article

Publication Date

10-1997

Publisher

Elsevier

Source Publication

Signal Processing

Source ISSN

0165-1684

Original Item ID

DOI: 10.1016/S0165-1684(97)00126-6

Abstract

The state estimation problem with observations which may or may not contain a signal at any sample time is considered from a covariance assignment viewpoint. The closed form solution for directly assigning steady state estimation error covariances and their assignability conditions are derived for the linear case. For the nonlinear case, upper bounds on the estimation error covariance are assigned. Examples are given for illustration in which the robustness of the proposed schemes are assessed.

Comments

Signal Processing, Vol. 62, No. 2 (October 1997): 215-228. DOI.

Edwin Yaz was affiliated with University of Arkansas at the time of publication.

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