A Self-Alignment Algorithm for SINS Based on Gravitational Apparent Motion and Sensor Data Denoising.

Area of Science:

• Navigation Systems
• Geophysics

Background:

• Initial alignment is crucial but challenging for Inertial Navigation Systems (INS).
• Existing methods using gravitational apparent motion can be sensitive to sensor noise.

Purpose of the Study:

• To propose a novel self-initial alignment algorithm for strapdown INS (SINS).
• To enhance alignment accuracy and robustness against sensor noise.

Main Methods:

• Utilizing gravitational apparent motion vectors at three distinct time points.
• Implementing an online Kalman filter for sensor data denoising.
• Developing a novel apparent motion reconstruction to prevent vector collinearity.

Main Results:

• The proposed method effectively performs initial alignment for SINS under static and swinging conditions.
• Accuracy achieved meets or approaches theoretical limits based on sensor precision.
• The Kalman filter and novel reconstruction significantly mitigate noise and collinearity issues.

Conclusions:

• The novel self-initial alignment algorithm offers improved performance for SINS.
• The integration of Kalman filtering and advanced motion reconstruction enhances robustness and accuracy.