  In the target tracking and its engineering applications, recursive state
estimation of the target is of fundamental importance. This paper presents a
recursive performance bound for dynamic estimation and filtering problem, in
the framework of the finite set statistics for the first time. The number of
tracking algorithms with set-valued observations and state of targets is
increased sharply recently. Nevertheless, the bound for these algorithms has
not been fully discussed. Treating the measurement as set, this bound can be
applied when the probability of detection is less than unity. Moreover, the
state is treated as set, which is singleton or empty with certain probability
and accounts for the appearance and the disappearance of the targets. When the
existence of the target state is certain, our bound is as same as the most
accurate results of the bound with probability of detection is less than unity
in the framework of random vector statistics. When the uncertainty is taken
into account, both linear and non-linear applications are presented to confirm
the theory and reveal this bound is more general than previous bounds in the
framework of random vector statistics.In fact, the collection of such
measurements could be treated as a random finite set (RFS).
