[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120908-en":3,"doc-seo-120908-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120908,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Applied To Global Navigation Satellite System Signal Condition Classification - Thesis Summary","Machine learning techniques are used for global navigation satellite system (GNSS) signal condition classification, with particular focus on radio frequency interference (RFI) in GNSS-Reflectometry (GNSS-R) data. The thesis organizes the work into three research topics: detecting Galileo satellite oscillator anomaly, detecting and mitigating RFI in GNSS-R signals, and detecting and classifying RFI observed by LEO satellites. A multi-chapter pipeline presents evaluation of performance, mitigation effectiveness, and comparisons across disturbance types.","MACHINE LEARNING APPLIED TO GLOBAL NAVIGATION SATELLITE SYSTEM SIGNAL CONDITION CLASSIFICATION  \nby  \nKahn-Bao Wu  \nB.A., National Taiwan Ocean University, 2017  \nM.A., National Taiwan University, 2019  \nA thesis submitted to the  \nFaculty of the Graduate School of the  \nUniversity of Colorado in partial fulfillment  \nof the requirement for the degree of  \nMaster of Science  \nDepartment of Aerospace Engineering Sciences  \n2023  \nCommittee Members:  \nProf. Y. Jade Morton  \nProf. Dennis Akos  \nProf. Yang Wang  \nWu, Kahn-Bao (M.A., Aerospace Engineering Sciences)  \nMachine Learning Applied To Global Navigation Satellite System Signal Condition Classification  \nThesis directed by Prof. Y. Jade Morton  \nThis 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.  \nCONTENTS  \nCHAPTER 1 INTRODUCTION ..................................................................... 1  \nCHAPTER 2 Automatic Detection of Galileo Satellite Oscillator Anomaly By Using A Machine Learning Algorithm ........................................... 3  \n2.1 Abstract..................................................................................................... 3  \n2.2 Introduction............................................................................................. 3  \n2.3 Methodology ............................................................................................ 4  \n2.3.1 Stage 1: Oscillator Anomaly Detection ................................... 5  \n2.3.2 Stage 2: Satellite Oscillator Anomaly Detection................... 8  \n2.3. Dataset Description ...................................................................... 8  \n2.3.4 Performance Evaluation .......................................................... 10  \n2.4 Detection Result ................................................................................... 11  \n2.5 Comparison with GPS satellite oscillator anomaly...................... 15  \n2.6 Conclusion.............................................................................................. 16  \n2.7 Reference................................................................................................ 17  \nCHAPTER 3 Detection and Mitigation of Radio Frequency Interference in GNSS-R Data ................................................................................ 19  \n3.1 Abstract................................................................................................... 19  \n3.2 Introduction........................................................................................... 19  \n3.3 Methodology .......................................................................................... 22  \n3.3.1 RFI Detection Method .............................................................. 22  \n3.3.2 RFI Mitigation Process.......","cbCaitmQYQtmKwmv","https://ap.wps.com/l/cbCaitmQYQtmKwmv","pdf",4782642,1,73,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Automatic Detection of Galileo Satellite Oscillator Anomaly By Using A Machine Learning Algorithm\n## 2.1 Abstract\n## 2.3 Methodology\n## 2.4 Detection Result\n## 2.5 Comparison with GPS satellite oscillator anomaly\n# Chapter 3 Detection and Mitigation of Radio Frequency Interference in GNSS-R Data\n## 3.1 Abstract\n## 3.3 Methodology\n## 3.5 Results\n## 3.6 Discussion\n# Chapter 4 Detection and Classification of Radio Frequency Interference Observed by LEO Satellites Using A Machine Learning Algorithm\n## 4.1 Abstract\n## 4.3 Methodology\n## 4.4 Detection Results","[{\"question\":\"What does the thesis focus on for GNSS signal condition classification?\",\"answer\":\"It applies machine learning to classify GNSS signal conditions, emphasizing radio frequency interference (RFI) in GNSS-Reflectometry (GNSS-R) data.\"},{\"question\":\"What is covered in the Galileo satellite oscillator anomaly detection work?\",\"answer\":\"It presents a two-stage detection method for Galileo satellite oscillator anomalies and discusses the significance of the results.\"},{\"question\":\"How is RFI handled in the GNSS-R signal processing pipeline?\",\"answer\":\"The thesis includes both detection and mitigation of RFI, analyzes mitigation performance, and extends machine learning classification toward LEO-observed RFI types.\"}]","Machine Learning Applied To Global Navigation Satellite System Signal Condition Classification - Thesis Summary | PDF",1785732628,184,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-applied-to-global-navigation-satellite-system-signal-condition-classification-thesis-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-applied-to-global-navigation-satellite-system-signal-condition-classification-thesis-summary/120908/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the thesis focus on for GNSS signal condition classification?","Question",{"text":75,"@type":76},"It applies machine learning to classify GNSS signal conditions, emphasizing radio frequency interference (RFI) in GNSS-Reflectometry (GNSS-R) data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is covered in the Galileo satellite oscillator anomaly detection work?",{"text":80,"@type":76},"It presents a two-stage detection method for Galileo satellite oscillator anomalies and discusses the significance of the results.",{"name":82,"@type":73,"acceptedAnswer":83},"How is RFI handled in the GNSS-R signal processing pipeline?",{"text":84,"@type":76},"The thesis includes both detection and mitigation of RFI, analyzes mitigation performance, and extends machine learning classification toward LEO-observed RFI types.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]