Abstract: In this talk, we present a novel technique for multiple Unmanned
Aerial Vehicles (UAVs) to cooperatively detect mobile RF (Radio
Frequency) emitting ground targets.  The distributed technique
maximizes the search capabilities of multiple UAVs using a hybrid
approach combining a set of intentional cooperative rules with
emerging properties of a swarm. We assume the UAVs are equipped
with low-precision RF direction finding sensors and the targets
may emit signals randomly with variable duration. Once a target is
detected, each UAV optimizes a cost function to determine whether
to participate in a cooperative localization task.  The cost
function balances between the completion of detecting all targets
(global search) in the search space and increasing the precision
of cooperatively locating already detected targets. A second cost
function is used to determine search patterns of each UAV.
Collectively, the cost functions for each UAV determines the
swarming behavior of multiple UAVs: (1) optimization of the number
of UAVs involved for locating targets, (2) search pattern to
detect all targets, and (3) computation of optimal UAV flight path
for individual UAV. We show the validity of our algorithm using
simulation results.