An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a...

Area of Science:

• Engineering
• Instrumentation

Background:

• Micro-electro-mechanical systems (MEMS) gyroscopes are susceptible to environmental influences and temperature variations, which affect their accuracy.
• Accurate heading determination is crucial in various applications, but temperature drift in MEMS gyroscopes poses a significant challenge.

Purpose of the Study:

• To develop an adaptive algorithm for MEMS gyroscopes that compensates for temperature drift and environmental influences.
• To enhance the precision and reliability of MEMS gyroscope measurements in dynamic temperature conditions.

Main Methods:

• An adaptive algorithm was developed using a simplified drift model and adaptable parameters.
• A strong tracking Kalman filter (STKF) was employed, with temperature drift model parameters updated dynamically.
• Integration with a compass provided environmental adaptation support for the algorithm.

Main Results:

• The algorithm demonstrated strong adaptability to changing temperatures, maintaining gyroscope precision.
• Heading errors were below 0.6° in static temperature experiments.
• In dynamic outdoor experiments, heading errors remained within a ±5° range, showing significant improvement over traditional KF and MLR methods.

Conclusions:

• The proposed adaptive algorithm effectively compensates for MEMS gyroscope temperature drift and environmental influences.
• The algorithm offers superior performance compared to conventional Kalman Filter (KF) and Multiple Linear Regression (MLR) methods.
• This approach significantly enhances the accuracy and robustness of MEMS gyroscopes in variable temperature environments.