Robust ε-contaminated gaussian filtering of discrete-time linear systems

Abstract

A robust slate estimation of multi-input single-output discrete-time linear systems is considered, where both the system disturbance and observation noise sequences contain outliers. The robust estimation problem is mathematically formulated for a special case assuming that the samples of the system disturbance and observation noise are from a known ε-contaminated gaussian density and a partially known ε-contaminated gaussian density, respectively. Through Monte Carlo simulations, the performance of the proposed robust filter is compared with that of the gaussian sum filter, which is the best non-linear filter when the densities of the underlying uncertainties are completely known. Comparison is also made between the proposed filter and some other available candidates.