Affiliated with the
Communication & Space
Sciences Laboratory

Nature Inspired Antenna Design

Genetically Designed Microstrip Antennas

Example of a microstrip stacked patch antenna. The antenna consists of a probe-fed microstrip patch antenna that is topped by a dielectric superstrate and a parasitic metallic patch. Compared to conventional single-layer designs, microstrip stacked patch antennas typically have a higher broadside gain and a wider bandwidth. 
The expanded view illustrates the main design parameters of the antenna, including the properties of the substrate and superstrate layers, dimensions of the driven patch and the parasitic patch, location of the probe excitation, etc..  These parameters can be adjusted to tailor the antenna for particular design objectives. 
 
VSWR for GA Optimized Stacked Patch Antenna


..: References :..

1-) Genetic Algorithm Optimization of Some Novel Broadband and Multiband Microstrip Antennas
by T. G. Spence, D. H. Werner and R. D. Groff
2004 IEEE International Symposium on Antennas and Propagation, Monterey, California, June 20-26.

ABSTRACT: The design of microstrip antennas usually involves specifying the values of several parameters that define its geometry and material characteristics. When there are a large number of parameters, it becomes necessary to use optimization techniques in the design of the antennas in order to maximize their performance. In this paper, we will discuss the design of some novel broadband and multiband microstrip antennas using Genetic Algorithm (GA) optimization techniques. A robust yet efficient optimization strategy that combines Model-Based Parameter Estimation (MBPE) with GA will also be introduced.





2-) Nature-based Design of Aperiodic Linear Arrays with Broadband Elements Using a Combination of Rapid Neural Network Estimation Techniques and Genetic Algorithms
by C. S. DeLuccia, and D. H. Werner
IEEE Antennas and Propagation Magazine, Vol. 49, No: 5, pp. 13 - 23, October 2007.


ABSTRACT: The developmetns presented in this paper address the challenge of determining the optimal element positions in nonuniformly spaced broadband phaased-array antennasin order to best meet desired performance criteria. Specifically, this is accomplished by introducing a new nature-based design technique that couples a robust genetic-algorithm (GA) optimizer with rapid neural-network (NN) estimation procedures. These provide performance criteria as functions of the element positions over the entire scanning range and bandwidth of operation. The objective of this GA-NN technique is to determine the optimal element position for a broadband aperiodic linear phased-array antenna in order to minimize element VSWRs and sidelobe levels. The NN estimation procedurs circumvent the need for computational intensive full-wave numerical simulations during the optimization process, which would ordinarily render such an optimization task impractical. The effectiveness of the new GA-NN design synthesis technique is demonstrated by considering an example where a nonuniform spaced linear phased array of ten stacked patch antennas is optimized for operation within a given bandwidth and scanning range.






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