Application of Evolutionary Algorithms and Neural Network Concepts to the Design of Low-Cost, Wideband Antenna Arrays
Abstract
This paper describes the application of biologically-inspired algorithms and concepts to the design of wideband antenna arrays. In particular, we address two specific design problems. The first involves the design of a constrained-feed network for a Rotman-lens beamformer. We implemented two evolutionary optimization (EO) approaches, namely a simple genetic algorithm (SGA) and a competent genetic algorithm. We conducted simulations based on experimental data, which effectively demonstrate that the competent GA outperforms the SGA (i.e., finds a better design solution) as the objective function becomes less specific and more general. The second design problem involves the implementation of polyomino-shaped subarrays for sidelobe suppression of large, wideband planar arrays. We use a modified screen-saver code to generate random polyomino tilings. A separate code assigns array values to each element of the tiling (i.e., amplitude, phase, time delay, etc.) and computes the corresponding far-field radiation pattern. In order to conduct a statistical analysis of pattern characteristics vs. tiling geometry, we needed a way to measure the similarity between two arbitrary tilings to ensure that our sampling of the tiling space was somewhat uniformly distributed. We ultimately borrowed a concept from neural network theory, which we refer to as the dot-product metric, to effectively categorize tilings based on their degree of similarity.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2007
- Accession Number
- ADA471346
Entities
People
- David E. Goldberg
- Michelle H. Champion
- Robert J. Mailloux
- Scott G. Santarelli
- Thomas M. Roberts
- Tian-li Yu
Organizations
- Air Force Research Laboratory