Teresa H. O'Donnell

An Impedance-Matched 2-Element Superdirective Array

With proper adjustment of the amplitude and phase of the element excitations, the directivity of an N-element end-fire array of isotropic radiators can approach N2 as the spacing between the elements approaches zero. To achieve this end-fire superdirectivity for a 2-element array, the excitation phase difference between the elements must closely approach 180deg. When closely spaced elements are driven nearly 180deg out-of-phase, the element radiation resistances approach zero, resulting in a high input Voltage Standing Wave Ratio (VSWR) and reduced radiation efficiency. At the same time, there is also a significant decrease in the operating bandwidth. In this letter we present the design of a 2-element, superdirective multiple arm folded monopole array that achieves a near 50 Omega input radiation resistance at each element, resulting in a matched input VSWR, higher radiation efficiency and therefore, a substantial increase in realized or overall efficiency.

Military antenna design using simple and competent genetic algorithms

Over the past decade, the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB has employed the simple genetic algorithm (SGA) as an optimization tool for a wide variety of antenna applications. Over roughly the same period, researchers at the Illinois Genetic Algorithm Laboratory (IlliGAL) at the University of Illinois at Urbana Champaign have developed GA design theory and advanced GA techniques called competent genetic algorithms-GAs that solve hard problems quickly, reliably, and accurately. Recently, under the guidance and direction of the Air Force Office of Scientific Research (AFOSR), the two laboratories have formed a collaboration, the common goal of which is to apply simple, competent, and hybrid GA techniques to challenging antenna problems. This paper is composed of two parts. The first part of this paper summarizes previous research conducted by AFRL at Hanscom for which SGAs were implemented to obtain acceptable solutions to several antenna problems. This research covers diverse areas of interest, including array pattern synthesis, antenna test-bed design, gain enhancement, electrically small single bent wire elements, and wideband antenna elements. The second part of this paper starts by briefly reviewing the design theory and design principles necessary for the invention and implementation of fast, scalable genetic algorithms. A particular procedure, the hierarchical Bayesian optimization algorithm (hBOA) is then briefly outlined, and the remainder of the paper describes collaborative efforts of AFRL and IlliGAL to solve more difficult antenna problems. In particular, recent results of using hBOA to optimize a novel, wideband overlapped subarray system to achieve -35 dB sidelobes over a 20% bandwidth. The problem was sufficiently difficult that acceptable solutions were not obtained using SGAs. The case study demonstrates the utility of using more advanced GA techniques to obtain acceptable solution quality as problem difficulty increases.

Efficient global optimization of a limited parameter antenna design

Efficient Global Optimization (EGO) is a competent evolutionary algorithm suited for problems with limited design parameters and expensive cost functions. Many electromagnetics problems, including some antenna designs, fall into this class, as complex electromagnetics simulations can take substantial computational effort. This makes simple evolutionary algorithms such as genetic algorithms or particle swarms very time-consuming for design optimization, as many iterations of large populations are usually required. When physical experiments are necessary to perform tradeoffs or determine effects which may not be simulated, use of these algorithms is simply not practical at all due to the large numbers of measurements required. In this paper we first present a brief introduction to the EGO algorithm. We then present the parasitic superdirective two-element array design problem and results obtained by applying EGO to obtain the optimal element separation and operating frequency to maximize the array directivity. We compare these results to both the optimal solution and results obtained by performing a similar optimization using the Nelder-Mead downhill simplex method. Our results indicate that, unlike the Nelder-Mead algorithm, the EGO algorithm did not become stuck in local minima but rather found the area of the correct global minimum. However, our implementation did not always drill down into the precise minimum and the addition of a local search technique seems to be indicated.

Applying EGO to large dimensional optimizations: a wideband fragmented patch example

Efficient Global Optimization (EGO) minimizes expensive cost function evaluations by correlating evaluated parameter sets and respective solutions to model the optimization space. For optimizations requiring destructive testing or lengthy simulations, this computational overhead represents a desirable tradeoff. However, the inspection of the predictor space to determine the next evaluation point can be a time-intensive operation. Although DACE predictor evaluation may be conducted for limited parameters by exhaustive sampling, this method is not extendable to large dimensions. We apply EGO here to the 11-dimensional optimization of a wide-band fragmented patch antenna and present an alternative genetic algorithm approach for selecting the next evaluation point. We compare results achieved with EGO on this optimization problem to previous results achieved with a genetic algorithm.

Optimum design of antennas using metamaterials with the efficient global optimization (EGO) algorithm

EGO is an evolutionary, data-adaptive algorithm which can be useful for optimization problems with expensive cost functions. Many antenna design problems qualify since complex computational electromagnetics (CEM) simulations can take significant resources. This makes evolutionary algorithms such as genetic algorithms (GA) or particle swarm optimization (PSO) problematic since iterations of large populations are required. In this paper we discuss multiparameter optimization of a wideband, single-element antenna over a metamaterial ground plane and the interfacing of EGO (optimization) with a full-wave CEM simulation (cost function evaluation).

Issues involved in developing a genetic algorithm methodology for optimizing the position of ship-board antennas

While genetic algorithms are powerful optimization tools, they typically require many function space evaluations. This makes their utilization limited when the time per evaluation is significant. We discuss one such application, the optimization of antenna positioning on ship-board platforms. We present the issues involved and propose intelligent preprocessing and genetic algorithm modifications which reduce both function evaluation time and the extent and complexity of the function space. While these strategies were developed for this particular application, most would be suitable for other complex military optimization problems.

Hybrid chromosome design for genetic optimization of a fragmented patch array antenna

Chromosome design has been shown to be a crucial element in developing genetic algorithms which approach global solutions without premature convergence. The consecutive positioning of parameters with high-correlations and relevance enhances the creation of genetic building blocks which are likely to persist across recombination to provide genetic inheritance. Incorporating positional gene relevance is challenging, however, in multi-dimensional design problems. We present a hybrid chromosome designed for optimizing a fragmented patch antenna which combines linear and two-dimensional gene representations. We compare previous results obtained with a linear chromosome to solutions obtained with this new hybrid representation.