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Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning.

Lina He^1,2, Hairui Zhou³, Yuanlan Wen⁴

¹School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China. hlnyh@hhu.edu.cn.

Sensors (Basel, Switzerland)

|June 29, 2019

Summary

This summary is machine-generated.

This study introduces an improved satellite clock prediction model for BeiDou navigation systems, enhancing real-time precise positioning accuracy and reducing convergence times for static and kinematic applications.

Keywords:

BeiDou satellite navigation system (BDS)generalized regression neural network precise point positioning satellite clock prediction

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Area of Science:

Satellite Navigation Systems
Geodesy and Geophysics
Artificial Intelligence in Geomatics

Background:

Precise real-time positioning services face limitations in accessibility due to cost, equipment, and coverage.
Accurate prediction of satellite clocks is crucial for enhancing real-time precise positioning.
Existing prediction models require improvement for widespread application.

Purpose of the Study:

To develop an improved satellite clock prediction model for BeiDou-2 satellites.
To optimize the model for satellite-specific characteristics.
To evaluate the model's performance in real-time precise positioning applications.

Main Methods:

Combined Spectrum Analysis (SA) and Generalized Regression Neural Network (GRNN) model.
Satellite-specific optimization of periodic terms and GRNN parameters (sample data length/interval, smooth factor).
Validation through comparison with existing models and precise estimations; application in Precise Point Positioning (PPP).

Main Results:

Predicted clock bias within ±0.5 ns over three hours, outperforming existing models.
Static PPP achieved >30.0% improvement in position accuracy, enabling sub-decimeter positioning.
Kinematic PPP showed shortened convergence time (0.5-1h) and enhanced accuracy (15.4-21.6%).

Conclusions:

The improved SA-GRNN model effectively enhances BeiDou-2 satellite clock prediction accuracy.
The model significantly improves both static and kinematic Precise Point Positioning performance.
This advancement offers a viable alternative for accessible real-time precise positioning.