Pub Date : 2023-10-18DOI: 10.1007/s10291-023-01551-0
Frederik Kuschewski, Jan Wüst, Markus Oswald, Tim Blomberg, Martin Gohlke, Jonas Bischof, Alex Boac, Tasmim Alam, André Bußmeier, Klaus Abich, Niklas Röder, Klaus Döringshoff, Jan Hrabina, Miroslava Holá, Jindřich Oulehla, Thilo Schuldt, Claus Braxmaier
Abstract One of the limiting factors for GNSS geolocation capabilities is the clock technology deployed on the satellites and the knowledge of the satellite position. Consequently, there are numerous ongoing efforts to improve the stability of space-deployable clocks for next-generation GNSS. The COMPASSO mission is a German Aerospace Center (DLR) project to demonstrate high-performance quantum optical technologies in space with two laser-based absolute frequency references, a frequency comb and a laser communication and ranging terminal establishing a link with the ground station located in Oberpfaffenhofen, Germany. A successful mission will strongly improve the timing stability of space-deployable clocks, demonstrate time transfer between different clocks and allow for ranging in the mm-range. Thus, the technology is a strong candidate for future GNSS satellite clocks and offers possibilities for novel satellite system architectures and can improve the performance of scientific instruments as well. The COMPASSO payload will be delivered to the international space station in 2025 for a mission time of 2 years. In this article, we will highlight the key systems and functionalities of COMPASSO, with the focus set to the absolute frequency references.
{"title":"COMPASSO mission and its iodine clock: outline of the clock design","authors":"Frederik Kuschewski, Jan Wüst, Markus Oswald, Tim Blomberg, Martin Gohlke, Jonas Bischof, Alex Boac, Tasmim Alam, André Bußmeier, Klaus Abich, Niklas Röder, Klaus Döringshoff, Jan Hrabina, Miroslava Holá, Jindřich Oulehla, Thilo Schuldt, Claus Braxmaier","doi":"10.1007/s10291-023-01551-0","DOIUrl":"https://doi.org/10.1007/s10291-023-01551-0","url":null,"abstract":"Abstract One of the limiting factors for GNSS geolocation capabilities is the clock technology deployed on the satellites and the knowledge of the satellite position. Consequently, there are numerous ongoing efforts to improve the stability of space-deployable clocks for next-generation GNSS. The COMPASSO mission is a German Aerospace Center (DLR) project to demonstrate high-performance quantum optical technologies in space with two laser-based absolute frequency references, a frequency comb and a laser communication and ranging terminal establishing a link with the ground station located in Oberpfaffenhofen, Germany. A successful mission will strongly improve the timing stability of space-deployable clocks, demonstrate time transfer between different clocks and allow for ranging in the mm-range. Thus, the technology is a strong candidate for future GNSS satellite clocks and offers possibilities for novel satellite system architectures and can improve the performance of scientific instruments as well. The COMPASSO payload will be delivered to the international space station in 2025 for a mission time of 2 years. In this article, we will highlight the key systems and functionalities of COMPASSO, with the focus set to the absolute frequency references.","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimation of water boundaries and evaluation of flood risk for a reservoir using GNSS multipath reflectometry","authors":"Xiaolei Wang, Xiufeng He, Minfeng Song, Dongzheng Jia","doi":"10.1007/s10291-023-01552-z","DOIUrl":"https://doi.org/10.1007/s10291-023-01552-z","url":null,"abstract":"","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1007/s10291-023-01553-y
Yuanxin Pan, Gregor Möller, Benedikt Soja
Abstract Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: They are either not easy to use or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modeling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total, five short baselines were formed and multipath errors were extracted from the postfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data and data from a low-cost device. We conclude that the ML-based multipath mitigation method is effective, easy to use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio, during model training.
{"title":"Machine learning-based multipath modeling in spatial domain applied to GNSS short baseline processing","authors":"Yuanxin Pan, Gregor Möller, Benedikt Soja","doi":"10.1007/s10291-023-01553-y","DOIUrl":"https://doi.org/10.1007/s10291-023-01553-y","url":null,"abstract":"Abstract Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: They are either not easy to use or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modeling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total, five short baselines were formed and multipath errors were extracted from the postfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data and data from a low-cost device. We conclude that the ML-based multipath mitigation method is effective, easy to use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio, during model training.","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1007/s10291-023-01545-y
Xiaodong Ren, Xuan Le, Dengkui Mei, Hang Liu, Xiaohong Zhang
{"title":"IROTI: a new index to detect and identify traveling ionospheric disturbances and equatorial plasma bubbles","authors":"Xiaodong Ren, Xuan Le, Dengkui Mei, Hang Liu, Xiaohong Zhang","doi":"10.1007/s10291-023-01545-y","DOIUrl":"https://doi.org/10.1007/s10291-023-01545-y","url":null,"abstract":"","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135970104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A new deep self-attention neural network for GNSS coordinate time series prediction","authors":"Weiping Jiang, Jian Wang, Zhao Li, Wudong Li, Peng Yuan","doi":"10.1007/s10291-023-01544-z","DOIUrl":"https://doi.org/10.1007/s10291-023-01544-z","url":null,"abstract":"","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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