GNSS World of China

Volume 46 Issue 3
Jun.  2021
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LYU Minghui, LI Wei, ZHANG Baocheng, CHAI Yanju. Refined stochastic model of combining elevation angle and SNR and its impact on precise point positioning in high latitude areas[J]. GNSS World of China, 2021, 46(3): 15-23, 53. doi: 10.12265/j.gnss.2020122101
Citation: LYU Minghui, LI Wei, ZHANG Baocheng, CHAI Yanju. Refined stochastic model of combining elevation angle and SNR and its impact on precise point positioning in high latitude areas[J]. GNSS World of China, 2021, 46(3): 15-23, 53. doi: 10.12265/j.gnss.2020122101
shu

Refined stochastic model of combining elevation angle and SNR and its impact on precise point positioning in high latitude areas

doi: 10.12265/j.gnss.2020122101
  • 1.

    Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan 430071, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Received Date: 2020-12-21
    Available Online: 2021-06-30
  • Publish Date: 2021-06-15
    Abstract
  • Principal component analysis method is used to determine the contribution of elevation angle and signal to noise ratio (SNR) in observation noise, and a refined Global Navigation Satellite System (GNSS) stochastic model is established based on the analysis results. The performance of the refined stochastic model is verified by using precision point positioning (PPP). It shows that the refined stochastic model leads to better positioning results in high latitude areas than traditional model that only takes into account elevation angle or SNR. The refined stochastic model is about 30% more accurate than elevation angle model, and about 20% better than SNR model. The accuracy of refined stochastic model improves most obvious in the zenith direction, and the improvements are about 38% and 24% with respect to the results of elevation angle model and SNR model, respectively. This study indicates that our new refined stochastic model is advantage to high-precision positioning accuracy in high latitude areas.

     

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