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Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms

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Abstract

The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of the PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data from 30 MGEX stations in the Asia–Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by \(n\)-fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality.

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Data availability

The GNSS observations are available in the IGS repository: ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/daily/rinex3. The satellite orbit, clock offset, ERP and DCB products from CODE MGEX center: http://ftp.aiub.unibe.ch/CODE_MGEX/. The multi-GNSS DCB products from CAS: ftp://ftp.gipp.org.cn/product/dcb/.

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Acknowledgments

The IGS, CODE, WHU and CAS are greatly acknowledged for providing the Multi-GNSS tracking data, SINEX coordinates, satellite orbit, ERP, clock offset and DCB products. We would also like to thank Zhongyang Zhao, an employee of **’an Honor Device Co., Ltd. for his suggestions on this paper.

Funding

This work was supported by the Programs of the National Natural Science Foundation of China (42127802), the Key R&D Program of Shaanxi Province (2022ZDLSF07-12), the Special Fund for Basic Scientific Research of Central Colleges (Grant No. CHD300102269305, CHD300102268305, CHD300102263401, Chang’an University).

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Authors and Affiliations

  1. College of Geology Engineering and Geomatics, Chang’an University, **’an, 710054, Shaanxi, China

    Mengyuan Li, Guanwen Huang, Le Wang & Wei **e

  2. Key Laboratory of Ecological Geology and Disaster Prevention, Ministry of Natural Resources, **’an, 710054, Shaanxi, China

    Guanwen Huang

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  1. Mengyuan Li
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  2. Guanwen Huang
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  3. Le Wang
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  4. Wei **e
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Contributions

ML conducted the experiments, prepared figures, and wrote the manuscript. GH developed the methodology and reviewed the manuscript. LW proposed this study and developed the methodology, and reviewed the manuscript. WX conducted the experiments and reviewed the manuscript. All authors were involved in discussions throughout the development.

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Correspondence to Guanwen Huang.

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Li, M., Huang, G., Wang, L. et al. Comprehensive classification assessment of GNSS observation data quality by fusing k-means and KNN algorithms. GPS Solut 28, 21 (2024). https://doi.org/10.1007/s10291-023-01557-8

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