  We take up optimality results for robust Kalman filtering from
Ruckdeschel[2001,2010] where robustness is understood in a distributional
sense, i.e.; we enlarge the distribution assumptions made in the ideal model by
suitable neighborhoods, allowing for outliers which in our context may be
system-endogenous/propagating or -exogenous/non-propagating, inducing the
somewhat conflicting goals of tracking and attenuation. Correspondingly, the
cited references provide optimally-robust procedures to deal with each type of
outliers separately, but in case of IO-robustness does not say much about the
implementation. We discuss this in more detail in this paper. Most importantly,
we define a hybrid filter combining AO- and IO-optimal ones, which is able to
treat both types of outliers simultaneously, albeit with a certain delay. We
check our filters at a reference state space model, and compare the results
with those obtained by the ACM filter Martin and Masreliez[1977], Martin[1979]
and non-parametric, repeated-median based filters Fried et al.[2006,2007].
