{"title":"Measuring GPS EIRP in Real-Time with a Spaceborne GNSS-Reflectometry Remote Sensing System","authors":"Tianlin Wang, C. Ruf","doi":"10.23919/USNC-URSINRSM51531.2021.9336441","DOIUrl":null,"url":null,"abstract":"This paper presents a unique technique which uses a spaceborne GNSS receiver system to measure GPS EIRP in real time and then uses it to improve the calibration of NBRCS for GNSS-Reflectometry observations. It includes two steps: precise gain calibration of satellite GNSS antenna using on-orbit measurements, and dynamic GPS EIRP calibration with a spaceborne system. For the CYGNSS mission, it successfully recovers the flagged measurements (~37% of the entire dataset) to be included in the standard science data products and further improves the mission's science data quality.","PeriodicalId":180982,"journal":{"name":"2021 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/USNC-URSINRSM51531.2021.9336441","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a unique technique which uses a spaceborne GNSS receiver system to measure GPS EIRP in real time and then uses it to improve the calibration of NBRCS for GNSS-Reflectometry observations. It includes two steps: precise gain calibration of satellite GNSS antenna using on-orbit measurements, and dynamic GPS EIRP calibration with a spaceborne system. For the CYGNSS mission, it successfully recovers the flagged measurements (~37% of the entire dataset) to be included in the standard science data products and further improves the mission's science data quality.