Abstract:
The risk-sensitive filter design problem with respect to the exponential mean-square criterion is considered for stochastic Gaussian systems with polynomial drift terms a...Show MoreMetadata
Abstract:
The risk-sensitive filter design problem with respect to the exponential mean-square criterion is considered for stochastic Gaussian systems with polynomial drift terms and intensity parameters multiplying diffusion terms in the state and observations equations. The closed-form suboptimal filtering algorithm is obtained by linearizing a nonlinear third degree polynomial system at the operating point and reducing the original problem to the optimal filter design for a first degree polynomial system. The reduced filtering problem is solved using quadratic value functions as solutions to the corresponding Fokker-Planck-Kolmogorov equation. The performance of the obtained risk-sensitive filter for stochastic third degree polynomial systems is verified in a numerical example against the mean-square optimal third degree polynomial filter and extended Kalman-Bucy filter, through comparing the exponential mean-square criteria values. The simulation results reveal strong advantages in favor of the designed risk-sensitive algorithm for large values of the intensity parameters.
Date of Conference: 08-10 July 2009
Date Added to IEEE Xplore: 09 October 2009
ISBN Information:
Print ISSN: 1085-1992