In this paper, we consider the problem of

filtering in relational hidden Markov models.

We present a compact representation for such models

and an associated logical particle filtering

algorithm. Each particle contains a logical

formula that describes a set of states.

The algorithm updates the formulae as new

observations are received.

Since a single particle tracks many states, this filter

can be more accurate than a traditional particle filter

in high dimensional state spaces, as we demonstrate

in experiments.