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.