A Novel MEMS Gyroscope In-Self Calibration Approach

Area of Science:

• Engineering
• Instrumentation
• Sensor Technology

Background:

• Traditional calibration of micro-electro-mechanical system (MEMS) gyroscopes often requires expensive, high-precision external equipment.
• Low-cost MEMS gyroscopes suffer from various errors, including bias, scale factor, and non-orthogonal errors, hindering their widespread application.

Purpose of the Study:

• To develop a novel, low-cost, and self-contained calibration method for hand-held MEMS gyroscopes.
• To eliminate the need for external high-precision calibration equipment.
• To accurately estimate gyroscope sensor errors through user-induced motion.

Main Methods:

• A Kalman filter-based algorithm was designed for gyroscope in-self calibration.
• System observability analysis was performed to determine optimal dynamic rotating conditions for error estimation.
• User hand rotation was utilized as the dynamic input for calibration.
• Simulated and practical experiments were conducted to validate the algorithm.

Main Results:

• The Kalman filter effectively estimated gyroscope bias error, scale factor error, and non-orthogonal error.
• Derived dynamic rotating conditions ensure sensor error observability.
• Experimental results validated the proposed calibration algorithm's effectiveness.
• The method demonstrated promising performance for low-cost MEMS gyroscope calibration.

Conclusions:

• The presented self-calibration approach offers a viable and cost-effective solution for MEMS gyroscopes.
• Hand-rotation-based calibration is feasible and accurate for addressing sensor errors.
• This method significantly reduces reliance on external calibration infrastructure.