Near-optimal, distributed edge colouring via the nibble method
ESA '95 Selected papers from the third European symposium on Algorithms
A performance comparison of multi-hop wireless ad hoc network routing protocols
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Sensor Fusion for Target Detection and Tracking
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Topology management in ad hoc networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
A cone-based distributed topology-control algorithm for wireless multi-hop networks
IEEE/ACM Transactions on Networking (TON)
Localized algorithms for energy efficient topology in wireless ad hoc networks
Mobile Networks and Applications
Ad Hoc Networking
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Networks of phased array radars are generally able to provide better counter stealth target detection and classification. Each radar sensor (or node) generates information which requires transmission to a central authority that is able to evaluate the information. This requires a communications network to be established to allow transmission of information to and from any node. Each radar node is limited by range and degree and relies on the formation of a multi-hop network to facilitate these transmissions. This paper presents a model whereby the radar beam itself is used in the formation of a multi-hop network. The phased array's multi-functional nature allows rapid switching between communications and radar function. A model of how the communication system could operate is presented, and an evolutionary optimisation algorithm based upon the concept of Pareto optimality is used for the topological design of the network. Finally, a simulation environment is presented to show the simulated performance of the communication model and designed networks.