Statistical Orbit Determination Algorithm for Satellites in Indian Navigation Constellation (NavIC): Towards Extended Ephemeris Technology for NavIC Receiver
{"title":"Statistical Orbit Determination Algorithm for Satellites in Indian Navigation Constellation (NavIC): Towards Extended Ephemeris Technology for NavIC Receiver","authors":"T. V. Ramanathan, R. A. Chipade","doi":"10.2478/arsa-2020-0003","DOIUrl":null,"url":null,"abstract":"Abstract Ephemerides are essential for the satellite positioning in Global Navigation Satellite Systems (GNSS) user receivers. Acquisition of navigation data and ephemeris parameters are difficult in remote areas as well as in challenging environments. Statistical orbit determination techniques can help to predict the orbital parameters in the absence of navigation data. The present study is a first step towards the solution for generating orbital parameters and predicting the satellite positions in the absence of navigation data for satellites in NavIC constellation. The orbit determination algorithm predicted the satellite position using single station navigation data. The perturbations affecting the satellite orbits in NavIC constellation were also studied and an algorithm using perturbation force models is proposed for the satellites in NavIC constellation. Extended Kalman Filter (EKF) was used to address the non-linear dynamics model of the perturbation forces and distance of the ground station from the centre of Earth was used as measurement to solve the measurement equation. The satellite orbits were predicted up to 1 hour using the single station navigation data. The root mean square error (RMSE) of 12.59 m and 13.03 m were observed for NavIC satellites in Geosynchronous and Geostationary orbits, respectively, after 1 hour. The Kolmogorov-Smirnov test used to assess the goodness of fit of the proposed EKF algorithm for orbit prediction was found to be significant at 1% level of significance.","PeriodicalId":43216,"journal":{"name":"Artificial Satellites-Journal of Planetary Geodesy","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Satellites-Journal of Planetary Geodesy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/arsa-2020-0003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 2
Abstract
Abstract Ephemerides are essential for the satellite positioning in Global Navigation Satellite Systems (GNSS) user receivers. Acquisition of navigation data and ephemeris parameters are difficult in remote areas as well as in challenging environments. Statistical orbit determination techniques can help to predict the orbital parameters in the absence of navigation data. The present study is a first step towards the solution for generating orbital parameters and predicting the satellite positions in the absence of navigation data for satellites in NavIC constellation. The orbit determination algorithm predicted the satellite position using single station navigation data. The perturbations affecting the satellite orbits in NavIC constellation were also studied and an algorithm using perturbation force models is proposed for the satellites in NavIC constellation. Extended Kalman Filter (EKF) was used to address the non-linear dynamics model of the perturbation forces and distance of the ground station from the centre of Earth was used as measurement to solve the measurement equation. The satellite orbits were predicted up to 1 hour using the single station navigation data. The root mean square error (RMSE) of 12.59 m and 13.03 m were observed for NavIC satellites in Geosynchronous and Geostationary orbits, respectively, after 1 hour. The Kolmogorov-Smirnov test used to assess the goodness of fit of the proposed EKF algorithm for orbit prediction was found to be significant at 1% level of significance.