| Summary: | This thesis investigates the novel idea of using evolutionary algorithms to optimise
control and design aspects of active array antenna systems. Active arrays differ from
most mechanically scanned antennas in that they offer the ability to control the shape of
their radiation pattern. As active arrays consist of a multiplicity of transmit and receive
modules (TRMs), the task of optimally controlling them in order to generate a desired
radiation pattern becomes difficult. The control problem is especially true of conformal
(non-planar) array antennas that require additional phase control to achieve good
radiation pattern performance.
This thesis describes a number of significant advances in the optimisation of array
antenna performance. Firstly a genetic algorithm (GA) is shown to be effective at
optimising both planar and conformal antenna performance. A number of examples are
used to illustrate and promote the basic optimisation concept. Secondly, in this thesis
the techniques are advanced to apply multiobjective evolutionary optimisation
algorithms to array performance optimisation. It is shown that Evolutionary Algorithms
allow users to simultaneously optimise many aspects of array performance without the
need to fine-tune a large number of weights. The multiple-objective analysis methods
shown demonstrate the advantages to be gained by holding knowledge of the Pareto
optimal solution set.
Thirdly, this thesis examines the problems of optimising the design of large (many
element) array antennas. Larger arrays are often divided into smaller sub-arrays for
manufacturing reasons and to promote formation of difference beam patterns for
monopulse operation. In the past, the partitioning has largely been left to trial-and-error
or simple randomisation techniques. This thesis describes a new and novel approach for
optimally subdividing both planar and conformal array antennas as well as improving
gain patterns in a single optimisation process. This approach contains a new method of
partitioning array antennas, inspired from a biological process and is also presented and
optimised using evolutionary algorithms. Additionally, the technique can be applied to
any size or shape of array antenna, with the processing load dependent on the number of
subarrays, rather than the number of elements.
Finally, the success of these new techniques is demonstrated by presenting a range of
performance optimised examples of planar and conformal array antenna installations
including examples of optimally evolved subarray partitions.
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