  In sparse target inference problems it has been shown that significant gains
can be achieved by adaptive sensing using convex criteria. We generalize
previous work on adaptive sensing to (a) include multiple classes of targets
with different levels of importance and (b) accommodate multiple sensor models.
New optimization policies are developed to allocate a limited resource budget
to simultaneously locate, classify and estimate a sparse number of targets
embedded in a large space. Upper and lower bounds on the performance of the
proposed policies are derived by analyzing a baseline policy, which allocates
resources uniformly across the scene, and an oracle policy which has a priori
knowledge of the target locations/classes. These bounds quantify analytically
the potential benefit of adaptive sensing as a function of target frequency and
importance. Numerical results indicate that the proposed policies perform close
to the oracle bound (<3dB) when signal quality is sufficiently high
(e.g.~performance within 3 dB for SNR above 15 dB). Moreover, the proposed
policies improve on previous policies in terms of reducing estimation error,
reducing misclassification probability, and increasing expected return. To
account for sensors with different levels of agility, three sensor models are
considered: global adaptive (GA), which can allocate different amounts of
resource to each location in the space; global uniform (GU), which can allocate
resources uniformly across the scene; and local adaptive (LA), which can
allocate fixed units to a subset of locations. Policies that use a mixture of
GU and LA sensors are shown to perform similarly to those that use GA sensors
while being more easily implementable.
