Date of Award

3-2022

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Sanjeev Gunawardena, PhD

Abstract

This research documents architecture and implementation of an enhanced interference detection and classification analysis system, using both a database and storage solution utilizing machine learning algorithms to detect changes in Carrier-to-Noise strength over multiple GNSS sites. The system uses publicly-available government supported receivers to detect interference, and built using FOSS packaged as a programming library through Python. Two algorithms are discussed in terms of enhancing interference detection using both non-machine learning and machine learning approaches. Two algorithms are also discussed which are used for classification of events. In addition, an approach to Large Scale data analytics is demonstrated via a Histogram-Based Multi-Receiver analysis algorithm. Finally, the efficacy of the product is demonstrated by analyzing latest data from multiple GNSS receiver sites. The performance characteristics of the system is documented along with details on hardware utilization. We then conclude with a discussion on future work for this research and the best recommended approaches.

AFIT Designator

AFIT-ENG-MS-22-M-064

Comments

Approved for public release, 88ABW-2022-0409.

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