Sensor Bias Estimation via Rotational Frame Transformation
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Solution Overview
Problem
Existing methods for calibrating sensors measuring continuous physical vector fields require user intervention and are not robust, especially when the bias changes over time or in environments with varying physical fields, leading to potential inaccuracies and the need for frequent recalibration.
Innovation Solution
An iterative method that estimates and corrects sensor bias in the background using rotational change of frame operators and high-pass filtering, leveraging data from embedded gyrometers and other sensors to improve accuracy and robustness, allowing for continuous calibration without user intervention and handling partial calibration scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user intervention methods are used for sensor calibration, then initial bias correction can be achieved, but the system cannot adapt to time-varying bias changes and requires frequent manual recalibration
Solution Approach 1:
The system performs self-calibration by automatically detecting bias changes using gyroscope data and trigonometric calculations, eliminating the need for user intervention. The calibration process occurs autonomously in the background during normal device operation, with the system serving itself to maintain measurement accuracy without external assistance.
Solution Approach 2:
The system continuously monitors sensor measurements and uses feedback from gyroscope data to detect bias changes. When variations exceed thresholds, the system automatically triggers recalibration using trigonometric relationships between accelerometer and gyroscope measurements, creating a closed-loop feedback mechanism that maintains precision adaptively.
2Adaptability or versatility
If continuous background calibration is implemented, then adaptability to bias changes is improved, but computational resources and processing time increase
Solution Approach 1:
Instead of continuous heavy computation, the system performs calibration periodically based on detected events. The trigonometric-based recalibration is triggered only when bias changes are detected through threshold comparisons of measurement variations, creating an event-driven periodic action that balances adaptability with energy efficiency.
Solution Approach 2:
The system performs partial calibration only when necessary, using selective trigonometric calculations based on detected bias changes. Rather than continuously recalibrating all sensor parameters, the system applies calibration only to affected measurement axes and only when variation thresholds are exceeded, reducing unnecessary computational overhead.
3Ease of operation
If automatic background calibration is used, then ease of operation is improved, but reliability in varying physical field environments may be compromised
Solution Approach 1:
The system performs preliminary validation checks before executing calibration, comparing measurement variations against thresholds and verifying gyroscope data quality. This preliminary action ensures that calibration is only performed when conditions are appropriate, preventing erroneous corrections in varying physical field environments and maintaining reliability.
Solution Approach 2:
The system continuously monitors measurement consistency and uses feedback to validate whether calibration should proceed. By comparing accelerometer and gyroscope data in real-time and detecting coherent patterns, the system ensures that automatic calibration occurs only when reliable data is available, maintaining robustness against environmental variations.
Data Source
AI summary
An iterative method determines a bias of a sensor for measuring a substantially continuous physical vector field in a reference frame, in which the sensor is linked in movement to a frame that is mobile in the reference frame. An iteration of the method includes:estimating a bias value in the mobile frame,correcting a measurement from the sensor of the estimated bias value, in the mobile frame,transforming the corrected measurement of the mobile frame in the reference frame, from a rotational change of frame operator between the mobile frame and the reference frame, andforming a criterion representative of a variation of the transformed corrected measurement.