Optimization and parameter estimation techniques have been employed for many years as a method of improving and exploring designs in numerous areas. As the designs of antennas and antenna arrays become more complex in nature, optimization techniques such as Bayesian estimation or genetic algorithms have become more necessary in the design process. These techniques provide methods for not only the design process, but also for operation simulations such as element failure corrections as well. This paper will deal with Bayesian optimization techniques for antenna and antenna array design as an alternative to other techniques. Through the use of Bayesian inference techniques, probability and information theory can be applied to a design problem to improve the operation within a range of specifications. Examples provided show that how this method allows for the examination of an entire parameter space of a linear array so that the best fitting solutions can be quickly and efficiently examined and improvements can be implemented.
2. Neal, R. M., "Slice sampling (with discussion)," Annals of Statistics, Vol. 31, 705-767, 2003.
3. Butz, A. R., "Alternative algorithm for Hilbert's space-filling curve," IEEE Trans. Comput., 424-426, 1971.
4. Lawder, J. K., "The application of space-filling curves to the storage and retrieval of multi-dimensional data," Ph.D. thesis, 2000.
5. Pacheco, P. S., A User's Guide to MPI, University of San Francisco Department of Mathematics, 1995.
6. Haupt, R. L. and S. E. Haupt, "Optimum population size and mutation rate for a simple real genetic algorithm that optimizes array factors," ACES Journal, Vol. 15, No. 2, 94-102, 2000.
7. Barbisch, B. J., D. H. Werner, and P. L. Werner, "A genetic algorithm optimization procedure for the design of uniformly excited and nonuniformly spaced broadband low sidelobe arrays," ACES Journal, Vol. 15, No. 2, 34-42, 2000.