This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

This thesis presents a novel antenna structure that satisfies the challenging requirements of an air coupled high speed ground penetrating radar (GPR). The desired GPR system is to achieve high spatial resolution and accurate inspection results while scanning at relatively high speed for highway pavement and bridge deck inspection. This work utilizes the Ultra Wide Band (UWB) antenna design to achieve both physical and electrical requirements imposed. The design procedure starts with a short survey to discuss typical UWB antennas used for GPR applications, and various tradeoffs of each type specifically when used for Air Coupled GPR applications. Our structure anatomy is presented, followed by a theory introduction that mainly focuses on achieving good impedance matching throughout the proposed antenna structure. A proof-of-concept MATLAB model is created to evaluate the preliminary physical dimensions that can achieve minimum reflections at antenna's feed point. These dimensions are then used in SolidWorks to create a 3D model that is imported later in HFSS to obtain accurate electromagnetic characteristics. Furthermore, fine tunings are performed to the antenna structure to optimize both gain and impedance matching. The SolidWorks 3-D structural model is finally used for antenna fabrication. The measurements recorded from the field experiments using the prototypes manufactured are compared to the simulation results confirming our initial findings. Both measurements and simulation results demonstrated very small reflection loss across the 700 MHz ~ 6 GHz frequency band with a very high directed gain and radiation efficiency.

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