  While the design of automated knowledge-based sensor scheduling is relevant
to many multi-target detection and tracking problems, tracking algorithms are
rarely built for this purpose and their outputs provide little flexibility for
the design of sensor policies. In this paper, we present an estimation
framework for stochastic populations in the context of multi-target estimation
problems. Fully probabilistic in nature, it allows for the evaluation of the
population of targets through statistical moments, as well as the assessment of
sensor observations through information-theoretical gain functions. We present
a principled solution derived from this framework addressing challenging
multi-target scenarios involving missed detections and false alarms, the filter
for Distinguishable and Independent Stochastic Populations, which propagates
information on previously-detected targets as well as yet-to-be-detected
targets while maintaining track continuity.
