Masters Theses

Keywords and Phrases

anomaly detection; deep learning; LSTM; RFI; statistics

Abstract

"Wireless devices are becoming increasingly pervasive across all aspects of society. Examples of such devices include radios, routers, mobile phones, tablets, and more. As the number of radio frequency (RF) devices continues to rise, so does the amount of interference and noise increase. This is why an efficient approach to interference detection is explored. Most research within this area has been done strictly within the frequency domain as viewing a signal within this domain provides many insights into what makes the signal. This has, however, led to the time domain being underutilized for this area of research.

To explore the time domain and its uses within radio frequency interference (RFI) detection we propose a lightweight program that requires knowledge of the known set of RF devices. The program utilizes a Long-Short Term Memory model to simulate a known radio set; it does this by training on a set of known signals interfered with each other. A custom statistical discriminator is then used to compare the simulated signal to the received signal. The output bounds of interference are then observed to determine how accurately our model detects and localizes interference" -- Abstract, p. iv

Advisor(s)

Madria, Sanjay Kumar

Committee Member(s)

Zawodniok, Maciej Jan, 1975
Nadendla, V. Sriram Siddhardh

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2024

Pagination

viii, 43 pages

Note about bibliography

Includes_bibliographical_references_(pages 35 & 40-42)

Rights

©2024 Luke Smith , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12431

Electronic OCLC #

1478162039

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