A Kalman filter-based short baseline RTK algorithm for single-frequency combination of GPS and BDS

Area of Science:

• Geomatics Engineering
• Satellite Navigation Systems
• Geodesy

Background:

• Emerging Global Navigation Satellite Systems (GNSS), such as the BeiDou Navigation Satellite System (BDS), increase the number of visible satellites for positioning.
• Real-time kinematic (RTK) algorithms benefit from multi-GNSS constellations to improve positioning precision and availability.
• Integrating GPS and BDS data presents an opportunity to enhance high-precision positioning performance.

Purpose of the Study:

• To propose and analyze a data processing model for dual GPS and BDS constellations within an RTK algorithm.
• To develop and investigate a Kalman filter-based algorithm for estimating float ambiguities in short baseline scenarios.
• To validate the effectiveness of the proposed multi-GNSS RTK model using real-world experimental data.

Main Methods:

• Development of a Kalman filter-based algorithm for dual GPS and BDS data processing.
• Estimation of float ambiguities for short baseline scenarios using the developed algorithm.
• Validation of the proposed model through experiments with real GPS and BDS data from zero and short baselines.

Main Results:

• The proposed dual GPS and BDS RTK algorithm achieves fixed baseline output with precision comparable to single-constellation RTK systems.
• Demonstrated significant improvements in the fixed rate and time to first fix compared to single-system approaches.
• Validation confirmed the model's effectiveness in processing multi-GNSS data.

Conclusions:

• The dual GPS and BDS RTK model enhances positioning availability and effectiveness by leveraging more visible satellites.
• The Kalman filter-based approach provides a robust method for high-precision positioning with multi-GNSS data.
• This research highlights the benefits of integrating multiple GNSS constellations for improved geodetic applications.