Gyroscope Zero-Rate Level Calibration via Machine Learning
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Solution Overview
Problem
Existing gyroscope technologies face challenges in dynamically updating zero-rate level calibration over time and temperature, with prior methods either focusing on static factory calibration or addressing only one component of drift, leading to high costs and limited accuracy.
Innovation Solution
The implementation of machine learning and iterative zero-rate level estimation techniques, utilizing stationary detection circuitry and temperature sensors to dynamically update zero-rate level compensation parameters, allowing for continuous calibration of gyroscope output signals even during use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory calibration with thermal cycling is used, then initial zero-rate level accuracy is improved, but manufacturing cost and time increase
Solution Approach 1:
The system performs preliminary thermal cycling calibration during factory production to establish initial compensation parameters. This preliminary action captures the dominant temperature-dependent drift characteristics, providing a solid baseline calibration that reduces the need for extensive field recalibration while maintaining manufacturing efficiency.
Solution Approach 2:
The gyroscope system performs self-calibration in the field by automatically detecting stationary periods, measuring actual zero-rate level drift, and updating compensation parameters without external intervention. This self-service capability maintains accuracy over time and temperature without requiring repeated factory-calibration-level resources.
2Measurement precision
If static factory calibration is used, then initial calibration accuracy is improved, but adaptability to long-term drift decreases
Solution Approach 1:
The system transitions from static factory calibration to dynamic field calibration by continuously monitoring gyroscope output during stationary periods. The compensation parameters are updated in real-time based on actual drift observations, allowing the system to adapt to long-term zero-rate level changes while maintaining calibration accuracy throughout the device lifetime.
Solution Approach 2:
The system implements feedback mechanisms where gyroscope output during stationary periods is fed back to update compensation parameters. This closed-loop approach allows the system to learn from actual drift behavior and continuously refine its calibration, bridging the gap between initial factory accuracy and long-term adaptability.
3Measurement precision
If comprehensive drift compensation is implemented, then long-term accuracy is improved, but computational complexity increases
Solution Approach 1:
The calibration system is segmented into distinct functional components: temperature sensing, stationary detection, drift measurement, and parameter updating. Each component handles a specific aspect of the calibration process, allowing for modular implementation that reduces overall system complexity while achieving comprehensive drift compensation through coordinated operation of these simplified subsystems.
Data Source
AI summary
One or more embodiments are directed to zero-rate level compensation systems. One such system includes stationary detection circuitry that receives gyroscope signals output by a gyroscope and determines whether the gyroscope is stationary based on the gyroscope signals. The stationary detection circuitry generates a stationary gyroscope signal indicating the gyroscope is stationary based on a determination that the gyroscope is stationary. A temperature sensor senses temperature and outputs temperature signals. Zero-rate level estimation circuitry receives the stationary gyroscope signal and a temperature signal associated with the stationary gyroscope signal, and iteratively estimates one or more zero-rate level compensation parameters based on the stationary gyroscope signal and the temperature signal.