Comparative Analysis of a Class of Robust State Estimation Algorithms

This paper deals with a problem of robust estimation of the state of discrete, linear, dynamical system in case when measurements are corrupted by non-Gaussian heavy-tailed noise. The solution of a mini-max approach to this problem is outlined. Two types of robust filters - unconditional and conditional - are presented. The results of digital Monte-Carlo simulations show high performance of the proposed robust filters in comparison with the linear Kalman filter.