Finite-horizon Anisotropy-based Estimation with Packet Dropouts

Abstract

In this paper, the estimation problem is studied for a class of linear discrete time-varying system with packet dropout in the framework of anisotropy-based theory. The extended vector of fragment of the disturbance sequence is from the set of random vectors with bounded anisotropy. The packet dropout effect is considered to be random and described by a binary switching sequence with Bernoulli distribution. The input-to-error dynamics is obtained for multiplicative noise system with mutually independent noises and input disturbance. By using anisotropy-based approach, the estimation problem is reduced to optimization one with convex constraints. The developed method provides the (sub)optimal estimator ensuring the boundedness of anisotropic norm for input-to-output error system. Numerical example is provided to demonstrate efficiency of proposed approach.