Title :
Approximate and limit results for nonlinear filters with small observation noise: the linear sensor and constant diffusion coefficient case
Author_Institution :
Technion Israel Inst. of Technol., Haifa, Israel
fDate :
6/1/1988 12:00:00 AM
Abstract :
Recursive approximations for a class of filtering problems are presented. This class is characterized by linear observation sensor, constant diffusion terms, and, for the multidimensional problem, potential-like conditions on the drift. For the case of small observation noise, these approximations are used to demonstrate the Gaussian limiting structure of the optimal nonlinear filter
Keywords :
approximation theory; filtering and prediction theory; Gaussian limiting structure; constant diffusion coefficient; drift; linear sensor; nonlinear filters; observation noise; optimal filter; recursive approximation; Algebra; Computer aided software engineering; Cramer-Rao bounds; Filtering; Gaussian noise; Gaussian processes; Multidimensional systems; Nonlinear filters; Sensor phenomena and characterization; Statistics;
Journal_Title :
Automatic Control, IEEE Transactions on
