Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration

Area of Science:

• Geomatics Engineering
• Satellite Navigation Systems
• Sensor Fusion

Background:

• Real-time kinematic (RTK) Global Positioning System (GPS) faces limitations in accuracy and continuity due to satellite visibility and baseline length.
• Precise Point Positioning (PPP) and BeiDou Navigation Satellite Systems (BDS) offer potential improvements but can suffer from reduced satellite visibility in dynamic environments.
• Inertial Navigation Systems (INS) are crucial for bridging navigation solutions during signal outages.

Purpose of the Study:

• To present the mathematical model for a robust extended Kalman filter (R-EKF) aided GEO/IGSO/GPS PPP/INS tight integration using raw GPS + BDS observations.
• To evaluate the impact of BDS Geosynchronous Earth Orbit (GEO) and Inclined Geo-Synchronous Orbit (IGSO) satellites, INS, and R-EKF on GPS PPP performance.
• To enhance the accuracy and continuity of navigation solutions for high-accuracy location-based services.

Main Methods:

• Development of a mathematical model for R-EKF aided GEO/IGSO/GPS PPP/INS tight integration.
• Utilizing raw observations from GPS and BDS satellites.
• Processing land-borne vehicle data to analyze the influences of different components on PPP accuracy and stability.
• Implementing a robust extended Kalman filter (R-EKF) to adjust data weights from GEO and IGSO satellites.

Main Results:

• Both GEO and IGSO satellites improve GPS PPP accuracy, with IGSO showing a more significant contribution.
• Integration with INS further enhances the accuracy and stability of PPP solutions.
• The R-EKF effectively adjusts the weights of GEO and IGSO data, leading to substantially improved positioning accuracy in the integrated system.

Conclusions:

• Combined GPS and BDS, augmented by INS and an R-EKF, provide a robust and accurate navigation solution.
• The R-EKF is essential for optimally utilizing data from different BDS satellite types (GEO, IGSO) and GPS.
• This integrated approach significantly overcomes the limitations of traditional GPS RTK and standalone PPP, especially in challenging environments.