  We consider the robust filtering problem for a state-space model with
outliers in correlated measurements. We propose a new robust filtering
framework to further improve the robustness of conventional robust filters.
Specifically, the measurement fitting error is processed separately during the
reweighting procedure, which differs from existing solutions where a jointly
processed scheme is involved. Simulation results reveal that, under the same
setup, the proposed method outperforms the existing robust filter when the
outlier-contaminated measurements are correlated, while it has the same
performance as the existing one in the presence of uncorrelated measurements
since these two types of robust filters are equivalent under such a
circumstance.
