A reduced dimensionality method for the steady-state Kalman filter

Casti J (1975). A reduced dimensionality method for the steady-state Kalman filter. In: Stochastic Systems: Modeling, Identification and Optimization, I. Mathematical Programming Studies, 5 (5). pp. 116-123 Germany: Springer Berlin Heidelberg. ISBN 978-3-642-00784-2 DOI:10.1007/BFb0120769.

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Abstract

We consider the standard Kalman filtering problem in which the dimension of the output (measurement) vector is p, while the dimension of the state-space for the process model is n. The usual approach to determination of the steady-state gain matrix involves solving an algebraic Riccati equation consisting of n(n+1)/2 quadratically nonlinear equations. In this article, we present an alternate equation for the optimal gain matrix, itself, continuing only np quadratically nonlinear components. Numerical results comparing the efficiency of the new equation with the standard approach are also given.

Item Type: Book Section
Research Programs: System and Decision Sciences - Core (SDS)
Depositing User: Romeo Molina
Date Deposited: 29 Nov 2016 14:27
Last Modified: 29 Nov 2016 14:28
URI: http://pure.iiasa.ac.at/14021

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