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Optimal state estimation using randomly delayed measurements without time stamping
Author(s) -
Yang Yuanhua,
Fu Minyue,
Zhang Huanshui
Publication year - 2013
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3016
Subject(s) - estimator , kalman filter , bounded function , covariance , state (computer science) , control theory (sociology) , computer science , uniform boundedness , mathematics , discrete time and continuous time , set (abstract data type) , mathematical optimization , algorithm , statistics , control (management) , mathematical analysis , artificial intelligence , programming language
SUMMARY This paper studies an optimal state estimation (Kalman filtering) problem under the assumption that output measurements are subject to random time delays caused by network transmissions without time stamping. We first propose a random time delay model which mimics many practical digital network systems. We then study the so‐called unbiased, uniformly bounded linear state estimators and show that the estimator structure is given based on the average of all received measurements at each time for different maximum time delays. The estimator gains can be derived by solving a set of recursive discrete‐time Riccati equations. The estimator is guaranteed to be optimal in the sense that it is unbiased with uniformly bounded estimation error covariance. A simulation example shows the effectiveness of the proposed algorithm. Copyright © 2013 John Wiley & Sons, Ltd.