NDLI: Sub-optimal risk-sensitive filtering for third degree polynomial stochastic systems

Content Provider	IEEE Xplore Digital Library
Author	Alcorta G, M.A. Basin, M. Anguiano, S.G. Maldonado, J.J.
Copyright Year	2009
Description	Author affiliation: Department of Physical and Mathematical Sciences, Autonomous University of Nuevo Leon, Cd. Universitaria, San Nicolas de los Garza, 66450, Mexico (Alcorta G, M.A.; Basin, M.; Anguiano, S.G.; Maldonado, J.J.)
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.
Starting Page	285
Ending Page	289
File Size	559278
Page Count	5
File Format	PDF
ISBN	9781424446018
ISSN	10851992
DOI	10.1109/CCA.2009.5280779
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-08
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Polynomials Stochastic systems Filters Nonlinear equations Algorithm design and analysis Filtering algorithms Noise level Control systems Noise robustness Mexico Council stochastic systems Risk-sensitive filtering
Content Type	Text
Resource Type	Article

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