Nonlinear Filter of Stochastic Approximation Type with Randomly Varying Truncations and its Acceleration

Abstract

State estimation of dynamical systems is one of the most important problems in control systems engineering. This paper deals with a stochastic approximation type nonlinear filter for state estimation of nonlinear stochastic dynamical systems. Though sufficient conditions for the convergence of this nonlinear filter has been provided, they are still restrictive. We relax the restriction on the observation systems by introducing the idea of randomly varying truncations. some modifications on the algorithm is also provided to accelerate the convergence.