Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman...

Area of Science:

• Robotics
• Navigation Systems
• Sensor Fusion

Background:

• Accurate heading angle estimation is critical for autonomous vehicle navigation and attitude determination.
• Existing methods often struggle with accuracy in challenging environments or rely on single-point GNSS solutions.

Purpose of the Study:

• To develop and validate a robust GNSS/INS/Barometer fusion method for high-accuracy autonomous vehicle heading angle estimation.
• To improve heading accuracy compared to traditional Extended Kalman Filter (EKF) approaches.

Main Methods:

• Integration of Global Navigation Satellite System (GNSS) dual antennas, Inertial Navigation System (INS), and barometer data.
• Implementation of an Adaptive Kalman Filter (AKF) for fusing INS errors and GNSS measurements.
• Application of Random Sample Consensus (RANSAC) for initial heading accuracy improvement and kinematic constraints for measurement model enhancement.

Main Results:

• Achieved root mean square (RMS) heading errors of 0.5418° in open environments and 0.636° in occluded environments.
• Demonstrated significant error reductions of 37.62% and 47.37% compared to the EKF method in respective environments.
• Validated the effectiveness of the proposed fusion method through experimental results.

Conclusions:

• The proposed GNSS/INS/Barometer fusion method significantly enhances autonomous vehicle heading angle accuracy.
• The integration of RANSAC and kinematic constraints further improves the robustness and precision of the navigation system.
• The method shows promise for reliable autonomous vehicle operation in diverse environmental conditions.