  This article concerns the performance limits of strictly causal state
estimation for linear systems with fixed, but uncertain, parameters belonging
to a finite set. In particular, we provide upper and lower bounds on the
smallest achievable gain from disturbances to the point-wise estimation error.
The bounds rely on forward and backward Riccati recursions -- one forward
recursion for each feasible model and one backward recursion for each pair of
feasible models. We give simple examples where the lower and upper bounds are
tight.
