Stochastic Approximation Type Nonlinear Filter with Randomly Varying Truncations

Abstract

State estimation of dynamical systems is one of the most important problems in control systems engineering. This paper is concerned with a nonlinear filter of stochastic approximation type for state estimation of nonlinear dynamical systems. Though sufficient conditions for the convergence of this nonlinear filter has been provided, they are still restrictive. It is shown that the restriction on the observation systems can be relaxed by introducing the idea of randomly varying truncations.