  Information-driven control can be used to develop intelligent sensors that
can optimize their measurement value based on environmental feedback. In object
tracking applications, sensor actions are chosen based on the expected
reduction in uncertainty also known as information gain. Random finite set
(RFS) theory provides a formalism for quantifying and estimating information
gain in multi-object tracking problems. However, estimating information gain in
these applications remains computationally challenging. This paper presents a
new tractable approximation of the RFS expected information gain applicable to
sensor control for multi-object search and tracking. Unlike existing RFS
approaches, the information gain approximation presented in this paper
considers the contributions of non-ideal noisy measurements, missed detections,
false alarms, and object appearance/disappearance. The effectiveness of the
information-driven sensor control is demonstrated through two multi-vehicle
search-while-tracking experiments using real video data from remote terrestrial
and satellite sensors.
