{"title":"利用机器学习决策树对大地测量全球导航卫星系统接收器进行振幅闪烁探测","authors":"Wang Li, Yiping Jiang, Hongyuan Ji, Wenqiang Wei","doi":"10.1186/s43020-024-00136-7","DOIUrl":null,"url":null,"abstract":"The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs). Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a strategy to accurately identify the ionospheric amplitude scintillation events utilizing the measurements collected with geodetic GNSS receivers. The proposed detection method relies on a pre-trained machine learning decision tree algorithm, leveraging the scintillation index computed from the carrier-to-noise data and elevation angles collected at 1-Hz. The experimental results using real data demonstrate a 99% accuracy in scintillation detection can be achieved. By combining advanced machine learning techniques with geodetic GNSS receivers, this approach is feasible to effectively detect ionospheric scintillation using non-scintillation GNSS receivers.\n","PeriodicalId":52643,"journal":{"name":"Satellite Navigation","volume":null,"pages":null},"PeriodicalIF":9.0000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Amplitude scintillation detection with geodetic GNSS receivers leveraging machine learning decision tree\",\"authors\":\"Wang Li, Yiping Jiang, Hongyuan Ji, Wenqiang Wei\",\"doi\":\"10.1186/s43020-024-00136-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs). Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a strategy to accurately identify the ionospheric amplitude scintillation events utilizing the measurements collected with geodetic GNSS receivers. The proposed detection method relies on a pre-trained machine learning decision tree algorithm, leveraging the scintillation index computed from the carrier-to-noise data and elevation angles collected at 1-Hz. The experimental results using real data demonstrate a 99% accuracy in scintillation detection can be achieved. By combining advanced machine learning techniques with geodetic GNSS receivers, this approach is feasible to effectively detect ionospheric scintillation using non-scintillation GNSS receivers.\\n\",\"PeriodicalId\":52643,\"journal\":{\"name\":\"Satellite Navigation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.0000,\"publicationDate\":\"2024-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Satellite Navigation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1186/s43020-024-00136-7\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Satellite Navigation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s43020-024-00136-7","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
Amplitude scintillation detection with geodetic GNSS receivers leveraging machine learning decision tree
The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs). Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a strategy to accurately identify the ionospheric amplitude scintillation events utilizing the measurements collected with geodetic GNSS receivers. The proposed detection method relies on a pre-trained machine learning decision tree algorithm, leveraging the scintillation index computed from the carrier-to-noise data and elevation angles collected at 1-Hz. The experimental results using real data demonstrate a 99% accuracy in scintillation detection can be achieved. By combining advanced machine learning techniques with geodetic GNSS receivers, this approach is feasible to effectively detect ionospheric scintillation using non-scintillation GNSS receivers.
期刊介绍:
Satellite Navigation is dedicated to presenting innovative ideas, new findings, and advancements in the theoretical techniques and applications of satellite navigation. The journal actively invites original articles, reviews, and commentaries to contribute to the exploration and dissemination of knowledge in this field.