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Proceedings Paper

Scalable self-organizing resource management for multi-function radars in a sensor network
Author(s): B. S. Weir; T. M. Sokol
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Paper Abstract

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.

Paper Details

Date Published: 16 April 2010
PDF: 7 pages
Proc. SPIE 7698, Signal and Data Processing of Small Targets 2010, 76980U (16 April 2010); doi: 10.1117/12.850913
Show Author Affiliations
B. S. Weir, Johns Hopkins Univ. (United States)
T. M. Sokol, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 7698:
Signal and Data Processing of Small Targets 2010
Oliver E. Drummond, Editor(s)

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