Networking radars to form a common air picture has provided a significant leap forward in tracking
capability. These advances have existed largely without any capability for coordinating the resources of the
networked sensors. In sensor-networking applications, multi-function radars, which have the ability to
allocate resources to different radar tasks such as surveillance and tracking, operate largely in a sensorcentric
fashion. That is, they make resource decisions based on a local-only tracking capability, and then pass
valid measurements to a sensor-networking function that compiles a common air picture. As the list of
required missions grows, sensors may no longer be able to operate in such a sensor-centric fashion, and will
need to leverage contributions of other networked sensors to meet all performance requirements.
This paper discusses the use of self-organizing principles for managing radar resources in a network-centric
fashion. Radars make resource allocation decisions relative to the common, multi-sensor track picture versus
a local track picture. By proper construction of the resource decision rules, the sensors adapt to an efficient
global resource allocation using indirect coordination. That is, knowledge of other sensors' contributions to
the common air picture is sufficient for the local sensor to apply local resources to tasks where it has a
competitive advantage. This approach can offer significant resource savings to the individual sensors and
improved tracking performance across the network. Further, the ability to coordinate tracking resources
across the network allows for much greater scalability as network size increases.