Design of unbiased reduced order filter for stochastic systems with sampled measurements

Abstract

The problem of synthesizing reduced order filter, that forms an unbiased estimate of linear functional from an unknown phase vector of a continuous time stochastic system with sampled measurements and additive noises, is considered in this work. The H 2 norm of the weighted transfer function from input noises to estimation error is used as a performance criterion. The method for reduced order filter design is based on the expression of the explicit dependence of performance criterion on unknown parameters in the canonical basis and the application of the non-convex optimization technique. Numerical simulations are performed to illustrate the proposed approach.