Peripheral-Free Calibration Method for Redundant IMUs Based on Array-Based Consumer-Grade MEMS Information Fusion.

Area of Science:

• Robotics and Autonomous Systems
• Sensor Fusion and Navigation

Background:

• Micro-electro-mechanical systems inertial navigation modules (M-IMU) offer reduced measurement singularities through data fusion.
• Existing random and fixed errors in M-IMUs limit overall navigation performance and accuracy.
• Calibration is essential to mitigate fixed errors and enhance M-IMU navigation precision.

Purpose of the Study:

• To propose a cost-effective and efficient calibration method for M-IMU fixed error parameter estimation.
• To improve the navigation accuracy of M-IMUs by addressing residual fixed errors.

Main Methods:

• Manual rotation of the M-IMU in various stationary attitudes.
• Application of the Levenberg-Marquardt (LM) calibration algorithm to optimize sensor cost functions.
• Adaptive support fusion of individual MEMS fixed error parameters for global M-IMU calibration.

Main Results:

• The proposed fusion calibration algorithm effectively estimates M-IMU fixed error parameters.
• Calibrated MEMS arrays showed approximately 10 dB improvement in measurement accuracy.
• Output data dispersion was reduced by approximately 8 dB compared to individual MEMS sensors.

Conclusions:

• The developed fusion calibration method enhances M-IMU navigation accuracy and data reliability.
• The approach demonstrates robustness and feasibility in multi-dimensional testing environments.
• This method offers a practical solution for improving MEMS-based inertial navigation systems.