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.
As the list of anti-air warfare and ballistic missile defense missions grows, there is an increasing need to
coordinate and optimize usage of radar resources across the netted force. Early attempts at this
optimization involved top-down control mechanisms whereby sensors accept resource tasking orders
from networked tracking elements. These approaches rely heavily on uncertain knowledge of sensor
constraints and capabilities. Furthermore, advanced sensor systems may support self-defense missions
of the host platform and are therefore unable to relinquish control to an external function.
To surmount these issues, the use of bottom-up emergent control techniques is proposed. The
information necessary to make quality, network-wide resource allocations is readily available to sensor
nodes with access to a netted track picture. By assessing resource priorities relative to the network
(versus local) track picture, sensors can understand the contribution of their resources to the netted
force. This allows the sensors to apply resources where most needed and remove waste. Furthermore,
simple local rules for resource usage, when properly constructed, allow sensors to obtain a globally
optimal resource allocation without direct coordination (emergence). These results are robust to partial
implementation (i.e., not all nodes upgraded at once) and failures on individual nodes (whether from
casualty or reallocation to other sensor missions), and they leave resource control decisions in the hands
of the sensor systems instead of an external function.
This paper presents independent research and development work on emergent control of sensor
resources and the impact to resource allocation and tracking performance.
The new generation of high resolution radars now being developed present a nonlinear tracking problem due to
a combination of long target ranges, small range errors, and relatively imprecise angle measurements. A variety of
filtering techniques have been proposed for ameliorating the effects of this non-linearity, including the (debiased)
converted measurements Kalman filter and the unscented filter. The benefits of these techniques are often described in
terms of tracking error; however, for handover of a dense target complex to downrange sensors, it is as important that the
errors be consistent with their ascribed covariance. The purpose of this paper is to identify when the nonlinear
conversion bias effects covariance consistency by examining the relative performance of various filtering techniques.
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