This paper investigates the state estimation problem for stochastic nonlinear differential systems with multiplicative noise. Our purpose is to combine the noise filtering properties of the Extended Kalman Filter with the global convergence properties of high-gain observers. We propose an observer-based algorithm and provide conditions under which a bound on the estimation error can be guaranteed. Simulations show that this algorithm reveals to be more efficient than the Extended Kalman Bucy filter for systems with large measurement errors.
Cacace, F., Germani, A., Palumbo, P. (2011). A state observer approach to filter stochastic nonlinear differential systems. In 50th IEEE Conference on Decision and Control & 11th European Control Conference (CDC-ECC 2011) (pp.7917-7922). IEEE [10.1109/CDC.2011.6160233].
A state observer approach to filter stochastic nonlinear differential systems
Palumbo, P
2011
Abstract
This paper investigates the state estimation problem for stochastic nonlinear differential systems with multiplicative noise. Our purpose is to combine the noise filtering properties of the Extended Kalman Filter with the global convergence properties of high-gain observers. We propose an observer-based algorithm and provide conditions under which a bound on the estimation error can be guaranteed. Simulations show that this algorithm reveals to be more efficient than the Extended Kalman Bucy filter for systems with large measurement errors.
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