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
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Zusammenfassung: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.< >
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0018-9286
DOI:10.1109/9.310043