A Fast Self-Calibration Method for Dual-Axis Rotational Inertial Navigation Systems Based on Invariant Errors.

Area of Science:

• Navigation Systems Engineering
• Mechatronics and Control Systems
• Geomatics and Surveying

Background:

• Dual-axis rotational inertial navigation systems (RINSs) require periodic calibration for sustained accuracy.
• Traditional inertial measurement unit (IMU) calibration is labor-intensive, time-consuming, and risks introducing new installation errors.
• Existing methods necessitate equipment disassembly, hindering operational readiness.

Purpose of the Study:

• To develop a system-level, automated self-calibration method for RINSs that eliminates the need for disassembly.
• To enable high-precision calibration by utilizing invariant error modeling.
• To significantly reduce calibration time and improve the overall accuracy of RINS.

Main Methods:

• Expressing navigation parameter errors in the inertial frame as invariant errors for rapid and accurate initial attitude estimation.
• Establishing angular velocity constraint equations using gimbal mechanism output and employing backtracking navigation to reuse sensor data.
• Designing a specific IMU rotation scheme to ensure full error observability, analyzed via piecewise constant system methods and singular value decomposition (SVD).

Main Results:

• The proposed method effectively estimates IMU errors and rotation axis installation errors within 12 minutes.
• Achieved estimation accuracy for errors is less than 4%.
• Post-calibration compensation significantly enhances the velocity and position accuracies of the RINS.

Conclusions:

• The developed self-calibration technique offers a highly efficient and accurate solution for RINS maintenance.
• Automated, in-situ calibration without disassembly is feasible and beneficial for RINS performance.
• This method addresses the limitations of traditional calibration, improving operational efficiency and navigational precision.