An Improved ACKF/KF Initial Alignment Method for Odometer-Aided Strapdown Inertial Navigation System.

Area of Science:

• Navigation Systems
• Control Theory
• Signal Processing

Background:

• Initial alignment of strapdown inertial navigation systems (SINS) is critical for land vehicles.
• Large misalignment angles during in-motion scenarios pose significant challenges for traditional alignment methods.

Purpose of the Study:

• To propose an improved adaptive cubature Kalman filter/Kalman filter (ACKF/KF) initial alignment method for SINS.
• To address the challenge of initial alignment with large misalignment angles in moving land vehicles.
• To achieve robust initial alignment even with unknown measurement noise.

Main Methods:

• Established SINS error equations using Euler angles for large misalignment.
• Deduced odometer/gyroscope dead reckoning (DR) error equations for linear observation.
• Developed an improved ACKF/KF method integrating Sage-Husa adaptive filtering.

Main Results:

• The proposed ACKF/KF method demonstrated superior accuracy and stability compared to EKF, CKF, and AEKF algorithms.
• Computer simulations validated the algorithm's performance.
• Vehicle tests confirmed the practical advantages of the developed method.

Conclusions:

• The improved ACKF/KF method effectively solves the in-motion initial alignment problem for SINS with large misalignment angles.
• The algorithm performs well even when measurement noise characteristics are unknown.
• This approach offers a reliable and accurate solution for land vehicle navigation.