Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


HarrisMSEEThesis.pdf (3.48 MB)

ETD Abstract Container

Abstract Header

Reconfigurable Array Control via Convolutional Neural Networks

Harris, Garrett A.
http://rave.ohiolink.edu/etdc/view?acc_num=wright1647969139925902

Abstract Details

2022, Master of Science in Electrical Engineering (MSEE), Wright State University, Electrical Engineering.
A method for the beam forming control of an array of reconfigurable antennas is presented. The method consists of using two parallel convolutional neural networks (CNNs) to analyze a desired radiation pattern image, or mask, and provide a suggestion for the reconfigurable element state, array shape, and steering weights necessary to obtain the radiation pattern. This research compares beam forming systems designed for three distinct element types: a patch antenna, a reconfigurable square spiral antenna restricted to a single reconfigurable state, and the fully reconfigurable square spiral. The parametric sweeps for the design of the CNNs are presented along with several examples of activation maps and mask-suggestion pairs. The beam forming error for each element type in both isolated and embedded cases is calculated over a set of 100 random masks. The network performance is reported as a root mean square error in steering direction and beam widths in both azimuth and elevation.
Michael A. Saville, Ph.D., P.E. (Advisor)
Josh Ash, Ph.D. (Committee Member)
Yan Zhuang, Ph.D. (Committee Member)
Hirsch Chizever, Ph.D. (Committee Member)
90 p.
Electrical Engineering
machine learning; artificial intelligence; antenna array; reconfigurable antennas; beamforming

Recommended Citations

Citations

  • Harris, G. A. (2022). Reconfigurable Array Control via Convolutional Neural Networks [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1647969139925902

    APA Style (7th edition)

  • Harris, Garrett. Reconfigurable Array Control via Convolutional Neural Networks. 2022. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1647969139925902.

    MLA Style (8th edition)

  • Harris, Garrett. "Reconfigurable Array Control via Convolutional Neural Networks." Master's thesis, Wright State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=wright1647969139925902

    Chicago Manual of Style (17th edition)

wright1647969139925902
214
© 2022, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.