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State estimation of stochastic singularly perturbed discrete‐time systems
Author(s) -
Kando Hisashi
Publication year - 1997
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/(sici)1099-1514(199701/02)18:1<15::aid-oca586>3.0.co;2-t
Subject(s) - kalman filter , discrete time and continuous time , control theory (sociology) , riccati equation , mathematics , white noise , state (computer science) , gaussian , filter (signal processing) , filtering problem , noise (video) , extended kalman filter , mathematical optimization , computer science , algorithm , statistics , control (management) , mathematical analysis , differential equation , artificial intelligence , physics , quantum mechanics , computer vision , image (mathematics)
In this paper the state estimation problem of singularly perturbed discrete‐time systems driven by Gaussian white noise is considered. The problem of simplification of both the Riccati gain and the filter gain is treated. On the basis of these results the reduced‐order filtering problem is studied. As a result it is shown that near‐optimal estimates can be achieved by using two reduced‐order current‐type Kalman filters. Furthermore, the near‐optimality is investigated. © 1997 John Wiley & Sons, Ltd.