Modified extended Kalman filtering and a real-time parallel algorithm for system parameter identification
- Texas A and M Univ., College Station, TX (USA). Dept. of Mathematics
- Rice Univ., Houston, TX (USA). Dept. of Electrical Engineering
The most popular real-time filtering algorithm for nonlinear systems is perhaps the extended Kalman 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, the author's 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.
- OSTI ID:
- 6934677
- Journal Information:
- IEEE Transactions on Automatic Control (Institute of Electrical and Electronics Engineers); (USA), Vol. 35:1; ISSN 0018-9286
- Country of Publication:
- United States
- Language:
- English
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