Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.565525
Title: Autonomous agents for multi-function radar resource management
Author: Charlish, A. B.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2011
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Abstract:
The multifunction radar, aided by advances in electronically steered phased array technology, is capable of supporting numerous, differing and potentially conflicting tasks. However, the full potential of the radar system is only realised through its ability to automatically manage and configure the finite resource it has available. This thesis details the novel application of agent systems to this multifunction radar resource management problem. Agent systems are computational societies where the synergy of local interactions between agents produces emergent, global desirable behaviour. In this thesis the measures and models which can be used to allocate radar resource is explored; this choice of objective function is crucial as it determines which attribute is allocated resource and consequently constitutes a description of the problem to be solved. A variety of task specific and information theoretic measures are derived and compared. It is shown that by utilising as wide a variety of measures and models as possible the radar’s multifunction capability is enhanced. An agent based radar resource manager is developed using the JADE Framework which is used to apply the sequential first price auction and continuous double auctions to the multifunction radar resource management problem. The application of the sequential first price auction leads to the development of the Sequential First Price Auction Resource Management algorithm from which numerous novel conclusions on radar resource management algorithm design are drawn. The application of the continuous double auction leads to the development of the Continuous Double Auction Parameter Selection (CDAPS) algorithm. The CDAPS algorithm improves the current state of the art by producing an improved allocation with low computational burden. The algorithm is shown to give worthwhile improvements in task performance over a conventional rule based approach for the tracking and surveillance functions as well as exhibiting graceful degradation and adaptation to a dynamic environment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.565525  DOI: Not available
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