Abstract
We consider a nonlinear plant which is modeled by an Ito stochastic differential eqnuation. Corresponding to the plant, the nonlinear observation mechanism is aslo set where the observation noise is modeled by a finitely additive white noise.
The main purpose of the present paper is to formulate the maximum a posteriori probability state estimate. By using the Onsager-Machlup functional, the a posteriori probability is derived. The basic equation for the MAP state estimate is derived with the aid of a dynamic programming approach.
The numerical procedure for realizing the recursive filtering is also proposed.
