Centralized Measurement Level Fusion of GNSS and Inertial Sensors for Robust Positioning and Navigation

Area of Science:

• Geomatics Engineering
• Navigation Systems
• Sensor Fusion

Background:

• High-precision location and navigation are crucial for intelligent vehicles, logistics, and augmented reality.
• Global Navigation Satellite System (GNSS) and Inertial Navigation Systems (INS) integration in smartphones offers a critical solution.
• Existing methods face limitations in challenging environments and harsh terrain.

Purpose of the Study:

• To develop and evaluate an algorithm combining GNSS and INS for enhanced navigation performance.
• To address the limitations of current navigation systems in adverse conditions.
• To improve the accuracy and reliability of location-based services.

Main Methods:

• Developed a novel algorithm integrating GNSS with a modified downdate algorithm (MDDA) for satellite selection.
• Implemented both loosely coupled (LC) and tightly coupled (TC) INS/GNSS configurations.
• Conducted vehicular experiments under diverse GNSS observation conditions to assess performance.

Main Results:

• The proposed tightly coupled (TC) algorithm with MDDA demonstrated significant improvements over the loosely coupled (LC) approach.
• Achieved over 90% reduction in 2D position root mean square error (RMSE).
• Demonstrated a 75% reduction in 3D position RMSE compared to standard Google solutions.

Conclusions:

• The integrated GNSS and INS approach, particularly the TC configuration with MDDA, substantially enhances navigation precision.
• This advancement unlocks the full potential of intelligent vehicle navigation systems.
• The developed algorithm offers a robust solution for demanding navigation applications.