A System-Level Self-Calibration Method for Installation Errors in A Dual-Axis Rotational Inertial Navigation System.

Area of Science:

• Engineering
• Navigation Systems
• Sensor Technology

Background:

• Dual-axis rotational inertial navigation systems (RINS) face challenges with installation errors.
• Traditionally, nonorthogonal sensor errors and misalignment with rotation axes are calibrated separately, complicating the process.

Purpose of the Study:

• To propose a system-level self-calibration method for dual-axis RINS that addresses all installation errors simultaneously.
• To enhance the accuracy and efficiency of RINS calibration by integrating error estimation.

Main Methods:

• Developed a Kalman filter-based approach to reestablish the measurement model for joint error estimation.
• Utilized the relationship between initial and subsequent inertial measurement unit (IMU) attitudes to calibrate accelerometer and IMU-to-axis errors.
• Employed the rotation mechanism's angular rate to calibrate gyro nonorthogonal installation errors.
• Designed a specific IMU rotation scheme to ensure observability of all installation errors.

Main Results:

• The proposed method effectively estimates nonorthogonal installation errors of accelerometers and gyros, along with IMU-to-axis installation errors.
• Simulation and experimental results validate the method's effectiveness in estimating installation errors.
• The unified approach avoids the need for complex, separate calibration processes.

Conclusions:

• The system-level self-calibration method offers a robust solution for dual-axis RINS installation error compensation.
• This integrated approach simplifies calibration and improves the overall performance of RINS.
• The findings contribute to more accurate and reliable inertial navigation solutions.