  Nowadays there is a growing research interest on the possibility of enriching
small flying robots with autonomous sensing and online navigation capabilities.
This will enable a large number of applications spanning from remote
surveillance to logistics, smarter cities and emergency aid in hazardous
environments. In this context, an emerging problem is to track unauthorized
small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in
large UAV networks. In contrast with current solutions mainly based on static
and on-ground radars, this paper proposes the idea of a dynamic radar network
of UAVs for real-time and high-accuracy tracking of malicious targets. To this
end, we describe a solution for real-time navigation of UAVs to track a dynamic
target using heterogeneously sensed information. Such information is shared by
the UAVs with their neighbors via multi-hops, allowing tracking the target by a
local Bayesian estimator running at each agent. Since not all the paths are
equal in terms of information gathering point-of-view, the UAVs plan their own
trajectory by minimizing the posterior covariance matrix of the target state
under UAV kinematic and anti-collision constraints. Our results show how a
dynamic network of radars attains better localization results compared to a
fixed configuration and how the on-board sensor technology impacts the accuracy
in tracking a target with different radar cross sections, especially in non
line-of-sight (NLOS) situations.
