Gyroscope Bias Calibration During Motion Using Low-Motion Segmentation
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Solution Overview
Problem
Existing gyroscope calibration methods require devices to be motionless, which is impractical for applications where continuous motion is expected, such as wearable devices or those that conserve power by turning off sensors during stillness, leading to inadequate bias estimation and user experience issues.
Innovation Solution
A method and system that automatically calibrate rate gyroscope bias in the presence of motion by identifying periods of low motion activity using multiple sensors like gyroscopes, accelerometers, and magnetometers, calculating a degree of motion metric, and updating the bias estimate based on variance analysis, allowing for continuous calibration and adaptation to changes due to aging, stress, and temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gyroscope calibration methods are used requiring the device to be motionless, then bias estimation accuracy is improved, but the method becomes impractical for wearable devices and applications with continuous motion
Solution Approach 1:
The system uses the device's own sensor measurements (accelerometer, magnetometer, gyroscope) to automatically identify low-motion periods and perform calibration without external equipment or user intervention. The sensor fusion algorithm autonomously determines calibration opportunities based on motion patterns detected by the device's own sensors.
Solution Approach 2:
The system dynamically changes the operational parameters of the gyroscope by switching between calibration mode (during low-motion periods) and normal operation mode (during high-motion periods). It adjusts the weighting and fusion of sensor data based on the detected motion state, enabling accurate bias estimation adaptively throughout the device lifecycle.
2Use of energy by moving object
If sensors are turned off during stillness to conserve power, then energy consumption is reduced, but bias calibration cannot be performed
Solution Approach 1:
Instead of continuously monitoring for calibration opportunities, the system periodically checks motion patterns using low-power accelerometer and magnetometer data. When a low-motion period is detected, it briefly activates the gyroscope for calibration, then returns to sleep mode, minimizing power consumption while maintaining calibration capability.
Solution Approach 2:
The system performs preliminary motion assessment using low-power sensors (accelerometer and magnetometer) before activating the high-power gyroscope. This preliminary check ensures that the gyroscope is only activated when truly necessary for calibration, optimizing power efficiency.
3Measurement precision
If factory calibration is performed, then initial bias accuracy is improved, but the calibration cannot adapt to aging, stress, and temperature changes
Solution Approach 1:
The system continuously monitors gyroscope measurements during low-motion periods and uses sensor fusion algorithms to detect bias drift caused by aging, stress, and temperature changes. It automatically updates calibration parameters based on this feedback, maintaining accuracy throughout the device lifecycle without requiring manual recalibration.
Solution Approach 2:
The calibration system transitions from a static factory-calibrated state to a dynamic self-updating state. It continuously adapts calibration parameters based on real-time environmental conditions and usage patterns, making the system resilient to aging and environmental variations.
4Measurement precision
If multiple sensors are used for motion detection, then motion detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the calibration process into distinct phases: motion detection phase (using accelerometer and magnetometer), calibration decision phase (determining if low-motion period exists), and calibration execution phase (activating gyroscope for bias estimation). This segmentation simplifies the overall algorithm by breaking down the complex sensor fusion task into manageable, sequential steps.
Data Source
AI summary
Devices and methods for automatic calibration of rate gyroscope bias are described. One example method includes receiving, while the device is in motion, a plurality of measurements from one or more sensors, including a gyroscope, in the device. In this example, the plurality of measurements is acquired over a time period. The method also includes identifying one or more time segments, within the time period, having a low motion activity of the device by comparing a computed value associated with a motion of the device to a predetermined threshold. The method further includes determining, based on one or more measurements acquired over the one or more time segments, a degree of motion metric for at least one of the one or more sensors, and determining, based on the degree of motion metric for the at least one of one or more sensors, an estimate of a bias of the gyroscope.