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.<...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 39; no. 8; pp. 1652 - 1656
Main Author: Ahmed, M.S.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.08.1994
Institute of Electrical and Electronics Engineers
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0018-9286
DOI:10.1109/9.310043