Title :
On maximum likelihood nonlinear filter under discrete-time observations
Author :
Aihara, Shin Ichi ; Bagchi, Arunabha
Author_Institution :
Sci. Univ. of Tokyo, Japan
Abstract :
The main purpose of the paper is to formulate the maximum likelihood state estimation problem correctly for a continuous-time nonlinear stochastic dynamical system with discrete-time observation mechanism. By using the Onsager-Machlup functional, a modified likelihood is introduced. The basic equation for the maximum likelihood state estimate is derived with the aid of a dynamic programming approach
Keywords :
continuous time systems; dynamic programming; filtering theory; maximum likelihood estimation; nonlinear dynamical systems; nonlinear filters; observers; stochastic systems; Onsager-Machlup functional; continuous-time nonlinear stochastic dynamical system; discrete-time observations; maximum likelihood nonlinear filter; maximum likelihood state estimation; Additive white noise; Educational institutions; Filtering; Maximum likelihood estimation; Motion estimation; Nonlinear equations; Nonlinear filters; Recursive estimation; State estimation; Stochastic systems;
Conference_Titel :
American Control Conference, 1999. Proceedings of the 1999
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4990-3
DOI :
10.1109/ACC.1999.782868
