東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine Learning Applied to Global N...

Wu, Kahn-Bao.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification./
作者:	Wu, Kahn-Bao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	74 p.
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30424961
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 74 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798379696726Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Global navigation satellite system

Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification.
LDR:02468nmm a2200373 4500 001 2397186
005 20240617111341.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379696726
035 $a (MiAaPQ)AAI30424961
035 $a AAI30424961
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wu, Kahn-Bao. $0 (orcid)0009-0001-2026-7605 $3 3766950
245 1 0 $a Machine Learning Applied to Global Navigation Satellite System Signal Condition Classification.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 74 p.
500 $a Source: Masters Abstracts International, Volume: 84-12.
500 $a Advisor: Morton, Y. Jade.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2023.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0051.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Remote sensing. $3 535394
653 $a Global navigation satellite system
653 $a GNSS-Reflectometry
653 $a Machine learning
653 $a Radio Frequency Interference
690 $a 0538
690 $a 0799
710 2 $a University of Colorado at Boulder. $b Aerospace Engineering. $3 1030473
773 0 $t Masters Abstracts International $g 84-12.
790 $a 0051
791 $a M.S.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30424961