Modified extended Kalman filtering and a real-time parallel algorithm for system parameter identification

A modification of the extended Kalman filter (EKF) algorithm, which is called MEKF for short, it introduced. The modification is achieved by an improved linearization procedure. For this purpose, a parallel computational scheme is recommended, and it has immediate applications to identifying unknown...

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Bibliographic Details
Published in:IEEE transactions on automatic control 1990-01, Vol.35 (1), p.100-104
Main Authors: Chui, C.K., Chen, G., Chui, H.C.
Format: Article
Language:English
Subjects:
ALGORITHMS
Applied sciences
Automatic control
Computer science
control theory
systems
COMPUTERIZED SIMULATION
Control system synthesis
Control systems
Control theory. Systems
Convergence
Exact sciences and technology
Filtering
GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Kalman filters
MATHEMATICAL LOGIC
Modelling and identification
NONLINEAR PROBLEMS
Parallel algorithms
PARALLEL PROCESSING
PROGRAMMING
Real time systems
SIMULATION 990200 > Mathematics & Computers
STOCHASTIC PROCESSES
Stochastic systems
Upper bound
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Summary:A modification of the extended Kalman filter (EKF) algorithm, which is called MEKF for short, it introduced. The modification is achieved by an improved linearization procedure. For this purpose, a parallel computational scheme is recommended, and it has immediate applications to identifying unknown system parameters of time-varying, linear, stochastic state-space models in real time. It should be noted that just like the EKF, the MEKF is also ad hoc in the sense that it is a real-time approximation method. Numerical examples with computer simulation are included to demonstrate the effectiveness of this procedure as compared to the EKF algorithm.< >
ISSN:0018-9286
1558-2523
DOI:10.1109/9.45155