McCaffrey, D., Harrison, R.F. and Banks, S.P.
(1998)
Asymptotically Optimal Nonlinear Filtering.
Research Report.
ACSE Research Report 734
.
Department of Automatic Control and Systems Engineering
Abstract
In this note we present a computationally simple algorithm for non-linear filtering. The algorithm involves solving, at a given point in state space, an algebraic Riccati equation. The coefficients of this equation vary with the given point in state space. We investigate conditions under which the state estimate given by this algorithm converges asymptotically to the first order minimum variance estimate given by the extended Kalman filter. We also investigate conditions for determining a region of stability for the filter given by this algorithm. The analysis is based on stable manifold theory and Hamilton-Jacobi-Bellman (HJB) equations. The motivation for introducing HJB equations is given by reference to the maximum likelihood approach to deriving the extended Kalman filter.
Metadata
Authors/Creators:
McCaffrey, D.
Harrison, R.F.
Banks, S.P.
Copyright, Publisher and Additional Information:
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