An Improved Online Fast Self-Calibration Method for Dual-Axis RINS Based on Backtracking Scheme

Area of Science:

• Engineering
• Navigation Systems
• Signal Processing

Background:

• High accuracy dual-axis rotational inertial navigation systems (RINS) require precise gyroscope and accelerometer calibration.
• Existing rotation modulation methods struggle with scale factor errors during vehicle maneuvers.
• Traditional self-calibration methods are too slow, taking hours to converge, hindering real-time applications.

Purpose of the Study:

• To develop a rapid and accurate online self-calibration method for RINS.
• To address limitations of existing methods in handling scale factor errors during dynamic conditions.
• To improve the convergence speed and accuracy of RINS calibration.

Main Methods:

• A 39-dimensional online calibration Kalman filtering (KF) model was developed to estimate all calibration parameters.
• The error relationship between calibration parameters and navigation error was mathematically derived.
• A backtracking filtering scheme was implemented to accelerate the calibration process.

Main Results:

• The proposed method significantly shortens the calibration time compared to traditional approaches.
• Simultaneous improvements in calibration accuracy for gyroscopes and accelerometers were achieved.
• The online KF model effectively estimates all calibration parameters.

Conclusions:

• The novel Kalman filtering and backtracking scheme offers a faster and more accurate solution for RINS self-calibration.
• This method meets the demand for quick response positioning and orientation in dynamic environments.
• The derived error relationships provide deeper insight into RINS calibration.