Gyroscope Data Processing for Most Sensor Types

Gyroscope data processing is a critical component in inertial navigation and attitude measurement systems. The core methodology involves implementing three types of corrections using MATLAB:

Bias Calibration In static conditions, collect data for a fixed duration and calculate the mean value as the bias offset. During dynamic operation, subtract this offset from raw data to eliminate inherent sensor bias. MATLAB implementation typically uses mean() function for static data analysis and vector subtraction for real-time correction.

Scale Factor Compensation Establish a linear relationship between output voltage and true angular rate by inputting known rotation rates (e.g., using turntable experiments). Apply MATLAB's least squares fitting (polyfit or regress functions) to determine the slope as scale factor. Subsequent data must be divided by this coefficient for unit normalization. The algorithm involves creating calibration curves from reference measurements.

Temperature Compensation (Optional) If gyroscope output shows temperature dependence, build compensation models for temperature-bias/scale factor relationships. Common methods include polynomial fitting (polyval function) or lookup table approaches. Synchronize temperature readings with data acquisition for real-time correction during operation.

Recommended Processing Pipeline: Raw Data → Bias Subtraction → Scale Factor Conversion → Temperature Compensation (if applicable) → Output Calibrated Angular Rate. MATLAB matrix operations enable efficient batch processing, while saving calibration parameters to configuration files facilitates reuse. For high-precision applications, consider adding Allan variance analysis (allanvar function) to evaluate noise characteristics and long-term stability performance.