Title :
The extended Kalman filter as an exponential observer for nonlinear systems
Author :
Reif, Konrad ; Unbehauen, Rolf
Author_Institution :
BMW AG, Munich, Germany
fDate :
8/1/1999 12:00:00 AM
Abstract :
We analyze the behavior of the extended Kalman filter as a state estimator for nonlinear deterministic systems. Using the direct method of Lyapunov, we prove that under certain conditions, the extended Kalman filter is an exponential observer, i.e., the dynamics of the estimation error is exponentially stable. Furthermore, we discuss a generalization of the Kalman filter with exponential data weighting to nonlinear systems
Keywords :
Kalman filters; Lyapunov matrix equations; discrete time filters; filtering theory; nonlinear filters; nonlinear systems; numerical stability; observers; Kalman filter generalization; Lyapunov direct method; Lyapunov matrix equation; Riccati matrix equation; difference equation; discrete-time extended Kalman filter; exponential data weighting; exponential observer; exponentially stable estimator error dynamics; nonlinear deterministic systems; nonlinear systems; state estimator; Approximation methods; Asymptotic stability; Estimation error; Filtering; Kalman filters; Nonlinear systems; Observers; State estimation; Stochastic processes; Stochastic systems;
Journal_Title :
Signal Processing, IEEE Transactions on
