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Machine learning-based multipath modeling in spatial domain applied to GNSS short baseline processing 基于机器学习的空间域多路径建模在GNSS短基线处理中的应用
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-16 DOI: 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.
多路径是阻碍卫星导航系统高精度数据处理的主要未建模误差源。传统的多路径缓解方法,如恒星滤波(SF)和多路径半球映射(MHM),都有一定的缺点:它们要么不容易使用,要么对多路径缓解不够有效。在本研究中,我们提出了一种基于机器学习(ML)的多路径缓解方法。将多径建模作为一种回归任务,在空间域内对多径误差进行方位角和高程拟合。我们收集了30天的1 Hz GPS数据来验证所提出的方法。总共形成5条短基线,并从拟合后残差中提取多径误差。利用目标日前5天的残差构建基于ml的多径模型以及观测域的SF和MHM模型,然后进行多径校正。发现XGBoost (XGB)方法优于SF和MHM方法。该方法对GPS P1、P2、L1和L2观测值的剩余降噪率最高,分别为24.9%、36.2%、25.5%和20.4%。采用基于xgb的多径修正后,东、北、上三分量的运动定位精度分别提高了1.6 mm、1.9 mm和4.5 mm,分别比原解提高了20.0%、17.4%和16.7%。利用30秒采样数据和低成本设备的数据进一步验证了基于ml的多路径模型的有效性。我们得出结论,基于机器学习的多径缓解方法是有效的,易于使用,并且可以通过在模型训练期间添加辅助输入特征(如信噪比)轻松扩展。
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引用次数: 0
Geocenter motion derived from multi-LEO precise orbit determination based on GNSS observations and dynamic force models 基于GNSS观测和动力模型的多leo精确定轨地心运动
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-15 DOI: 10.1007/s10291-023-01546-x
Keke Zhang, Xingxing Li, Weiping Jiang, Yuanchen Fu, Yongqiang Yuan, Jiaqing Lou, Wei Zhang
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引用次数: 0
IROTI: a new index to detect and identify traveling ionospheric disturbances and equatorial plasma bubbles IROTI:一种探测和识别电离层扰动和赤道等离子体气泡的新指数
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-12 DOI: 10.1007/s10291-023-01545-y
Xiaodong Ren, Xuan Le, Dengkui Mei, Hang Liu, Xiaohong Zhang
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引用次数: 0
A high-precision ZTD interpolation method considering large area and height differences 一种考虑大面积高差的高精度ZTD插值方法
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-11 DOI: 10.1007/s10291-023-01547-w
Qingzhi Zhao, Wei Wang, Zufeng Li, Zheng Du, Pengfei Yang, Wanqiang Yao, Yibin Yao
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引用次数: 1
A composite clock for robust time–frequency signal generation system onboard a navigation satellite 导航卫星上鲁棒时频信号产生系统的复合时钟
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-11 DOI: 10.1007/s10291-023-01542-1
Xiao Yi, Shitao Yang, Richang Dong, Qianyi Ren, Tao Shuai, Guang Li, Wenbin Gong
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引用次数: 0
Assessing all-frequency GPS/Galileo/BDS PPP-RTK in GNSS challenging environments 在GNSS挑战环境中评估全频率GPS/Galileo/BDS PPP-RTK
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-11 DOI: 10.1007/s10291-023-01543-0
Jianghui Geng, Ran Zeng, Jiang Guo
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引用次数: 0
A new deep self-attention neural network for GNSS coordinate time series prediction 一种新的GNSS坐标时间序列预测深度自关注神经网络
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-10 DOI: 10.1007/s10291-023-01544-z
Weiping Jiang, Jian Wang, Zhao Li, Wudong Li, Peng Yuan
{"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":null,"pages":null},"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}
引用次数: 0
Bw-ICEEMDAN/NExT-ERA method of data processing for dynamic monitoring of a super high-rise TV tower based on GNSS-RTK technique 基于GNSS-RTK技术的超高层电视塔动态监测数据处理方法Bw-ICEEMDAN/NExT-ERA
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-09 DOI: 10.1007/s10291-023-01548-9
Chunbao Xiong, Zhi Shang, Wen Chen, Meng Wang
{"title":"Bw-ICEEMDAN/NExT-ERA method of data processing for dynamic monitoring of a super high-rise TV tower based on GNSS-RTK technique","authors":"Chunbao Xiong, Zhi Shang, Wen Chen, Meng Wang","doi":"10.1007/s10291-023-01548-9","DOIUrl":"https://doi.org/10.1007/s10291-023-01548-9","url":null,"abstract":"","PeriodicalId":12788,"journal":{"name":"GPS Solutions","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094846","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}
引用次数: 0
Initial performance assessment of Galileo High Accuracy Service with software-defined receiver 软件定义接收机伽利略高精度服务的初步性能评估
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-09 DOI: 10.1007/s10291-023-01540-3
Peiyuan Zhou, Guorui Xiao, Lan Du
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引用次数: 1
A new approach for estimating geocenter motion based on BDS-3 plane-specific orbit error correction model 基于BDS-3特定平面轨道误差修正模型的地心运动估计新方法
1区 地球科学 Q1 Earth and Planetary Sciences Pub Date : 2023-10-01 DOI: 10.1007/s10291-023-01541-2
Lina He, Xiangxiang He, Yu Huang, Chenxu Yang
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引用次数: 0
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