%0 Thesis
%9 Doctoral
%A Charlish, A.B.
%B Department of Electronic and Electrical Engineering
%D 2011
%F discovery:1334115
%I UCL (University College London)
%P 172
%T Autonomous agents for multi-function radar resource management
%U https://discovery.ucl.ac.uk/id/eprint/1334115/
%X 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.