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NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning.

Sensors (Basel, Switzerland)·2021
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Precise Position Estimation Using Smartphone Raw GNSS Data Based on Two-Step Optimization.

Taro Suzuki1

  • 1Future Robotics Technology Center, Chiba Institute of Technology, Chiba 2750016, Japan.

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Summary

This study introduces a novel method for precise smartphone Global Navigation Satellite System (GNSS) positioning. The technique achieves decimeter-level accuracy by optimizing velocity and position estimation separately, outperforming existing methods.

Keywords:
GNSSGPSlocalizationsmartphonetime-differenced carrier phase

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Area of Science:

  • Geomatics Engineering
  • Satellite Navigation Systems
  • Mobile Sensing

Background:

  • Smartphone Global Navigation Satellite System (GNSS) observations are inherently noisy due to antenna limitations, hindering conventional high-precision positioning.
  • Environments with GNSS signal shielding, such as tunnels and urban canyons, introduce data outliers and availability issues crucial for accurate positioning.

Purpose of the Study:

  • To develop a high-precision positioning method for smartphones using raw GNSS data, specifically addressing noise and outlier challenges.
  • To achieve decimeter-level positioning accuracy in the Google Smartphone Decimeter Challenge (GSDC) by leveraging factor graph optimization.

Main Methods:

  • A two-step factor graph optimization (FGO) approach was employed, separating velocity and position estimation.
  • Velocity was initially estimated from Doppler observations, with outlier exclusion and interpolation.
  • Position was subsequently estimated using time-differenced carrier phase (TDCP) with the optimized velocity as a constraint.

Main Results:

  • The proposed method achieved a final horizontal positioning accuracy of 1.229 meters.
  • This accuracy secured first place in the Google Smartphone Decimeter Challenge (GSDC).

Conclusions:

  • The developed two-step FGO method effectively mitigates noise and outliers in smartphone GNSS data.
  • The approach demonstrates the feasibility of achieving high-precision positioning using readily available smartphone sensors.