IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p412-d736361.html
   My bibliography  Save this article

Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area

Author

Listed:
  • Yongjun Lee

    (Department of Aerospace Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea)

  • Byungwoon Park

    (Department of Aerospace Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea)

Abstract

As the necessity of location information closely related to everyday life has increased, the use of global navigation satellite systems (GNSS) has gradually increased in populated urban areas. Contrary to the high necessity and expectation of GNSS in urban areas, GNSS performance is easily degraded by multipath errors due to high-rise buildings and is very difficult to guarantee. Errors in the signals reflected by the buildings, i.e., multipath and non-line-of-sight (NLOS) errors, are the major cause of the poor accuracy in urban areas. Unlike other GNSS major error sources, the reflected signal error, which is a user-dependent error, is difficult to differentiate or model. This paper suggests training a multipath prediction model based on support vector regression to obtain a function of the elevation and azimuth angle of each satellite. To extract an unbiased multipath from the GNSS measurements, the clock error of high-elevation QZSS was estimated, and the clock offset with other constellations was also calculated. A nonlinear multipath map was generated, as a result of training with the extracted multipaths, by a Support Vector Machine, which appropriately reflected the geometry of the building near the user. The model was effective at improving the urban area positioning accuracy by 58.4% horizontally and 77.7% vertically, allowing us to achieve a 20 m accuracy level in a deep urban area, Teheran-ro, Seoul, Korea.

Suggested Citation

  • Yongjun Lee & Byungwoon Park, 2022. "Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area," Mathematics, MDPI, vol. 10(3), pages 1-15, January.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:412-:d:736361
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/412/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/3/412/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pedro J. Correa-Caicedo & Horacio Rostro-González & Martin A. Rodriguez-Licea & Óscar Octavio Gutiérrez-Frías & Carlos Alonso Herrera-Ramírez & Iris I. Méndez-Gurrola & Miroslava Cano-Lara & Alejandro, 2021. "GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles," Mathematics, MDPI, vol. 9(21), pages 1-18, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong-Kyeong Lee & Yebin Lee & Byungwoon Park, 2023. "Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    2. O-Jong Kim & Changdon Kee, 2023. "Wavelet and Neural Network-Based Multipath Detection for Precise Positioning Systems," Mathematics, MDPI, vol. 11(6), pages 1-22, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:412-:d:736361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.