Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD

Abstract

In Infrared Search and Track (IRST) systems, clutter in the image hinders target detection especially in point-source target scenarios. Currently there is not a standardized metric for quantifying background clutter. Many clutter metrics have been proposed, but none have demonstrated effectiveness or compatibility for point-source targets. Factors such as environment conditions, detection algorithm, and correlation coefficient to probability of detection (PD) and false alarm (PFA) are the main considerations in determining the effectiveness of clutter metrics. Determining the most successful metric will increase Air Force Test and Evaluation (T&E) units’ capability by providing additional information on test conditions and environments of IRST systems. Additionally, the PD of a Convolutional Neural Network (CNN) based on the U-Net architecture compared to matched filter and threshold detection algorithms determined the effectiveness of CNNs for point-source target detection.

AFIT Designator

AFIT-ENG-MS-23-M-065

Comments

A 12-month embargo was observed.

Approved for public release: 88ABW-2023-0181

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