In-Motion Gyroscope Bias Calibration via Linear Fitting
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Solution Overview
Problem
Conventional methods for calibrating gyroscope sensors are inadequate when the device is in motion, as they rely on stationary data, which is often unavailable, and fail to account for time constraints, necessitating a more sophisticated method for estimating bias during dynamic conditions.
Innovation Solution
A system and method that utilize a computing device with gyro sensors, a GPS receiver, and a magnetometer to calibrate gyroscope sensors in motion by identifying a time period with a predetermined pattern, computing the orientation, and estimating bias through linear fitting of angular representations, allowing for continuous calibration during motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple averaging methods are used to estimate gyroscope bias, then calibration is easy to implement, but the method fails when stationary data is unavailable
Solution Approach 1:
The patent transitions from static calibration methods to dynamic calibration methods that work during device motion. The system identifies time periods with predetermined motion patterns (e.g., linear motion, constant velocity) and performs bias estimation during these dynamic states, enabling calibration without requiring the device to be stationary.
Solution Approach 2:
The patent changes the calibration approach by using linear fitting of angular representations instead of simple averaging. This involves computing orientation from gyroscope signals, identifying time periods with specific motion characteristics, and using linear regression on angular data to estimate bias, which is more sophisticated than simple averaging but works during motion.
2Measurement precision
If sophisticated methods like linear fitting are used to estimate bias during motion, then calibration accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the time period into specific intervals with predetermined motion patterns. By identifying and isolating time periods where the device exhibits known motion characteristics (e.g., constant velocity, linear acceleration), the system can apply linear fitting only to these segmented portions, reducing the overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent applies calibration only during specific time periods when predetermined motion patterns are detected, rather than continuously. This partial action approach reduces computational complexity by performing sophisticated bias estimation only when necessary, while using simpler methods or previously computed values during other periods.
3Measurement precision
If calibration is performed continuously to remove bias, then orientation accuracy is maintained, but time constraints are violated
Solution Approach 1:
The patent implements periodic calibration by continuously monitoring for time periods with predetermined motion patterns and performing bias estimation only when such patterns are detected. This periodic approach maintains orientation accuracy by regularly updating bias estimates during suitable intervals without requiring continuous calibration computation.
Solution Approach 2:
The system performs preliminary identification of suitable time periods with predetermined motion patterns before executing the bias estimation. By pre-identifying intervals where calibration can be effectively performed based on motion characteristics, the system prepares in advance and executes calibration only when conditions are favorable, optimizing the use of time.
Data Source
AI summary
Systems and methods to calibrate a gyroscope based on a motion of a predetermined characteristic. Data representing gyro sensor signals during a period of time is stored. A portion of the time period is identified, during which portion the gyroscope is subjected to a motion of the predetermined characteristic. Using the stored data the gyro signals are integrated with respect to time to calculate orientation of the gyroscope as a function of time. Deviation of a characteristic of the orientation of the gyroscope as the function of time during the portion of time period for the motion of the predetermined characteristic is determined to identify a component of bias in the gyro signals. The bias component is removed from the data to re-calculate the gyroscope orientation, and possible to further calculate the deviation in the re-calculated orientation and to identify a further bias component in the gyro signals.