A Kalman Filter for SINS Self-Alignment Based on Vector Observation

Area of Science:

• Navigation Systems
• Inertial Navigation
• Signal Processing

Background:

• Strapdown inertial navigation systems (SINS) require accurate self-alignment for optimal performance.
• Traditional self-alignment methods can be susceptible to noise in observation vectors, affecting accuracy and convergence.
• Existing methods may struggle with reducing random noise effectively.

Purpose of the Study:

• To develop a novel self-alignment method for SINS that improves accuracy and convergence rate.
• To reduce the impact of random noise on observation vectors during the self-alignment process.
• To enhance the overall reliability and efficiency of SINS alignment.

Main Methods:

• A self-alignment method based on the -method is investigated.
• An improved method integrates gravitational apparent motion to form apparent velocity, reducing observation vector noise.
• A novel Kalman filter with adaptive filter technology is proposed, transforming self-alignment into attitude estimation using quaternion and observation vectors via a linear pseudo-measurement equation.

Main Results:

• The proposed Kalman filter method significantly improves self-alignment accuracy.
• A new parameter recognition and apparent gravitation reconstruction algorithm enhances convergence rate and reduces noise influence.
• Simulations and turntable tests confirm sound alignment results with lower standard variances, higher accuracy, and faster convergence.

Conclusions:

• The novel self-alignment method using an adaptive Kalman filter offers superior performance for SINS.
• The integrated approach effectively mitigates noise, leading to more reliable navigation data.
• The developed technique provides a faster and more accurate alignment solution compared to existing methods.