國立虎尾科技大學 |

Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification./
Author:	Wu, Kahn-Bao.
Description:	1 online resource (74 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
Subject:	Aerospace engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379696726

Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification.
Wu, Kahn-Bao.

Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification. - 1 online resource (74 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of Colorado at Boulder, 2023.

Includes bibliographical references

This thesis focuses on machine learning (ML) techniques applied to global navigation satellite system (GNSS) signal condition classification or Radio Frequency Interference (RFI) in GNSS-Reflectometry (GNSS-R). The thesis consists of three individual research topics, namely, (1) Automatic Detection of Galileo Satellite Oscillator Anomaly By Using A Machine Learning Algorithm, (2) Detection and Mitigation of Radio Frequency Interference in GNSS-R Data and (3) Detection and Classification of Radio Frequency Interference Observed by LEO Satellites Using A Machine Learning Algorithm. Chapter 2 presents a two-stage detection method for Galileo satellite oscillator anomalies and discusses the significance of the results. Chapter 3 addresses the process of detecting and mitigating RFI in GNSS-R signals and analyzes the mitigation performance of the results. Chapter 4 extends the ML classifier from Chapter 2 and applies it to the GNSS-R signal direct signal classification. The detection result of the trained ML model for different types of disturbances, including RFI, oscillator anomaly, and ionosphere disturbance, is demonstrated. The thesis concludes in Chapter 5, which summarizes the three research topics and provides the contribution of the research.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379696726Subjects--Topical Terms:

686400
Aerospace engineering.
Subjects--Index Terms:

Global navigation satellite systemIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification.
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520 $a This thesis focuses on machine learning (ML) techniques applied to global navigation satellite system (GNSS) signal condition classification or Radio Frequency Interference (RFI) in GNSS-Reflectometry (GNSS-R). The thesis consists of three individual research topics, namely, (1) Automatic Detection of Galileo Satellite Oscillator Anomaly By Using A Machine Learning Algorithm, (2) Detection and Mitigation of Radio Frequency Interference in GNSS-R Data and (3) Detection and Classification of Radio Frequency Interference Observed by LEO Satellites Using A Machine Learning Algorithm. Chapter 2 presents a two-stage detection method for Galileo satellite oscillator anomalies and discusses the significance of the results. Chapter 3 addresses the process of detecting and mitigating RFI in GNSS-R signals and analyzes the mitigation performance of the results. Chapter 4 extends the ML classifier from Chapter 2 and applies it to the GNSS-R signal direct signal classification. The detection result of the trained ML model for different types of disturbances, including RFI, oscillator anomaly, and ionosphere disturbance, is demonstrated. The thesis concludes in Chapter 5, which summarizes the three research topics and provides the contribution of the research.
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