Modified Extended Kaiman Filtering and a Real-Time Parallel Algorithm for System Parameter Identification

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Original languageEnglish
Pages (from-to)100-104
Journal / PublicationIEEE Transactions on Automatic Control
Volume35
Issue number1
Publication statusPublished - Jan 1990
Externally publishedYes

Abstract

The most popular real-time filtering algorithm for nonlinear systems is perhaps the extended Kaiman filter which will be called EKF for brevity. In this note, a modification of the EKF algorithm, which will be called MEKF for short, is 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 as the EKF, our MEKF is also ad hoc in the sense that it is a real-time approximation method. Numerical examples with computer simulation are included in this note to demonstrate the effectiveness of this new procedure over the EKF algorithm. © 1990 IEEE