  In multi-object stochastic systems, the issue of sensor management is a
theoretically and computationally challenging problem. In this paper, we
present a novel random finite set (RFS) approach to the multi-target sensor
management problem within the partially observed Markov decision process
(POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS,
and the multi-Bernoulli filter is used in conjunction with two different
control objectives: maximizing the expected R\'enyi divergence between the
predicted and updated densities, and minimizing the expected posterior
cardinality variance. Numerical studies are presented in two scenarios where a
mobile sensor tracks five moving targets with different levels of
observability.
