Date of Award
3-2023
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Robert F. Mills, PhD
Abstract
This research focuses on the development of machine learning networks that can identify and classify airborne targets using their radar cross section response. Simulation and measurement data for five targets was collected using Altair's CadFEKO software, and the Air Force Institute's Compact Radar Range. Three machine learning models were trained using simulation data, and evaluated using the collected measurement data. Variability is introduced to the training data by applying random gaussian noise to simulation results. Gaussian noise is added to the measurement data prior to evaluation in-order to model "hostile noise jamming." Network performance is measured against a baseline performance of 20% - representative of a random guess over five targets. Two out three evaluated models were able to exceed the baseline performance, showing promise for this method of target classification.
AFIT Designator
AFIT-ENG-MS-23-M-055
DTIC Accession Number
AD1340950
Recommended Citation
Rofrano, Matthew M., "Classifying Open-air Target Measurements Using Simulation-trained Convolutional Neural Networks" (2023). Theses and Dissertations. 6937.
https://scholar.afit.edu/etd/6937
Comments
An embargo was observer for posting this thesis on AFIT Scholar.
Approved for public release. PA case number 88ABW-2023-0180