A new algorithm for state estimation of stochastic linear discrete systems

A novel algorithm is proposed for state estimation of linear discrete-time systems. The procedure performs explicit minimization of the innovation variance and is based upon the principle of pseudo linear regression (PLR) method. Sufficient conditions for algorithm convergence are also derived.<...

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Veröffentlicht in:	IEEE transactions on automatic control Jg. 39; H. 8; S. 1652 - 1656
1. Verfasser:	Ahmed, M.S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.08.1994 Institute of Electrical and Electronics Engineers
Schlagworte:	Applied sciences Computer science; control theory; systems Control theory. Systems Convergence Covariance matrix Exact sciences and technology Kalman filters Linear regression Minimization methods Modelling and identification Noise measurement Riccati equations State estimation Stochastic systems Technological innovation Linear system Simulation Minimization Discrete system Algorithm State estimation Stochastic system Convergence
ISSN:	0018-9286
Online-Zugang:	Volltext
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