Document Type
Honors Thesis
Date of Award
Spring 2024
Abstract
Military systems require monitoring a wide frequency range of the RF spectrum to detect and monitor enemy targets. A vital component of that is calculating the direction of arrival of enemy radars. The use of compressive sensing architectures like the Nyquist Folding Receiver (NyFR) allow for computationally cheap capturing of wideband signals; however, these techniques make traditional direction of arrival (DOA) estimation techniques difficult. Given the advantages of compressive sensing, this research aims to apply machine learning (ML) techniques to multi-arm spiral antennas to estimate the incoming DOA. Software like MATLAB was used to simulate the multi-arm spiral antenna and NyFR, allowing training of an ML algorithm using freely available tools like PyTorch.
Advisor
Gerald Fudge
Recommended Citation
Pearson, Samuel, "Estimating Direction of Arrival for Multi-Arm Spiral Antennas Using Machine Learning" (2024). Honors Theses. 246.
https://lair.etamu.edu/honorstheses/246
