Extended Kalman filter-based robust roll angle estimation method for spinning vehicles.

Area of Science:

• Engineering
• Geomatics
• Navigation Systems

Background:

• Vehicle attitude measurement is crucial for motion monitoring and safety.
• Spin motion complicates attitude angle coupling, impacting navigation and control.
• Existing Global Navigation Satellite System (GNSS) methods often fail with discontinuous signal power due to loop lock loss.

Purpose of the Study:

• To develop a robust roll angle estimation method for vehicles using GNSS signals.
• To address the challenge of discontinuous signal reception in attitude measurement.
• To improve the accuracy and stability of vehicle roll angle estimation.

Main Methods:

• A robust estimation method employing the Tukey weight function for GNSS signal power.
• Analysis of GNSS signal characteristics and the geometric relationship between signal power and roll angle.
• Experimental verification on a rotating platform using a single-patched antenna GNSS receiver.

Main Results:

• The proposed robust estimation method significantly improves roll angle accuracy compared to the traditional Least Squares (LS) method.
• Estimation errors were 6.57° (68% confidence) and 15.49° (95% confidence), versus 11.38° and 37.31° for LS.
• The algorithm's accuracy is independent of vehicle rotational speed, demonstrating robustness.

Conclusions:

• The robust estimation method enhances vehicle roll angle measurement accuracy, especially under gross measurement errors and discontinuous signal reception.
• The proposed algorithm provides high-precision and stable roll angle estimation suitable for various vehicle dynamics.
• GNSS-based attitude measurement can be made more reliable with advanced signal processing techniques.