  A robust fixed-lag smoothing approach is proposed in the case there is a
mismatch between the nominal model and the actual model. The resulting robust
smoother is characterized by a dynamic game between two players: one player
selects the least favorable model in a prescribed ambiguity set, while the
other player selects the fixed-lag smoother minimizing the smoothing error with
respect to least favorable model. We propose an efficient implementation of the
proposed smoother. Moreover, we characterize the corresponding least favorable
model over a finite time horizon. Finally, we test the robust fixed-lag
smoother in two examples. The first one regards a target tracking problem,
while the second one regards a parameter estimation problem.
