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Efficient optimization of reconfigurable parasitic antenna arrays using geometrical considerations

Reconfigurable aperture (RECAP) antennas can be considered a generalization of the reconfigurable antenna concept, consisting of a large array of reconfigurable elements and supporting many applications like adaptive matching, frequency agility, and beam- and null-formation. Although providing similar functions to phased arrays, the non-linear and non-convex nature of the objective function requires global search methods such as genetic algorithms or particle swarm optimization, which may be inappropriate for real-time, in-situ optimization. To overcome this difficulty, a first-order model for parasitic RECAPs is developed herein, whose simple geometric interpretation leads to straightforward and useful solutions for beamforming and nullsteering. It is also demonstrated how the first-order solution can be used to seed an efficient local optimizer to find exact solutions to the complicated full-order objective function. The utility of the method is demonstrated through numerical examples and performance is compared with a genetic algorithm.