An information-geometric approach to sensor management is introduced that is
based on following geodesic curves in a manifold of possible sensor
configurations. This perspective arises by observing that, given a parameter
estimation problem to be addressed through management of sensor assets, any
particular sensor configuration corresponds to a Riemannian metric on the
parameter manifold. With this perspective, managing sensors involves navigation
on the space of all Riemannian metrics on the parameter manifold, which is
itself a Riemannian manifold. Existing work assumes the metric on the parameter
manifold is one that, in statistical terms, corresponds to a Jeffreys prior on
the parameter to be estimated. It is observed that informative priors, as arise
in sensor management, can also be accommodated. Given an initial sensor
configuration, the trajectory along which to move in sensor configuration space
to gather most information is seen to be locally defined by the geodesic
structure of this manifold. Further, divergences based on Fisher and Shannon
information lead to the same Riemannian metric and geodesics.Comment: 4 pages, 3 figures, to appear in Proceedings of the IEEE
International Conference on Acoustics, Speech, and Signal Processing, March
201