In-use Accelerometer Calibration via Adaptive Data Fitting
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Solution Overview
Problem
Conventional accelerometer calibration methods are impractical for mobile devices as they require lab equipment and a flat, horizontal surface, and fail to account for bias drift due to environmental changes, leading to inaccurate sensor fusion and attitude estimation.
Innovation Solution
An in-use calibration methodology that uses mathematical models, such as spheres and ellipsoids, to adaptively select and fit accelerometer data, eliminating the need for lab equipment and allowing calibration during normal device use, with selection rules to reject linear acceleration and centripetal effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional factory calibration methods are used, then initial measurement precision is improved, but the calibration cannot be performed in-use and bias drift occurs over time
Solution Approach 1:
The system performs self-calibration by automatically detecting gravity vectors during normal device usage. The accelerometer calibrates itself without external intervention by processing data from various device orientations the user naturally encounters, eliminating the need for factory calibration environments while maintaining accuracy.
Solution Approach 2:
The system collects and processes accelerometer data in the background during normal device operation to perform calibration before it is needed. By continuously gathering data from various orientations and performing calculations in advance, the system prepares accurate calibration parameters without interrupting user workflow.
2Measurement precision
If factory calibration is performed on a flat horizontal surface, then initial calibration accuracy is improved, but the method is not adaptable to mobile device usage conditions
Solution Approach 1:
The system transitions from static calibration (requiring the device to be stationary on a flat surface) to dynamic calibration that works during normal mobile device usage. By processing accelerometer data from various moving orientations and applying motion detection algorithms, the system performs calibration in dynamic conditions rather than requiring static laboratory environments.
Solution Approach 2:
The calibration system becomes universal by working across all device orientations and usage scenarios. Instead of requiring a specific flat horizontal surface configuration, the system can calibrate the accelerometer regardless of how the user holds or moves the device, making the calibration process accessible in any real-world situation.
3Measurement precision
If conventional calibration methods are used, then initial bias accuracy is improved, but bias drift occurs due to temperature and environmental changes
Solution Approach 1:
The system implements continuous feedback by repeatedly performing calibration calculations as new accelerometer data becomes available during device usage. The calibration parameters are continuously refined based on incoming data, allowing the system to detect and compensate for bias drift caused by temperature changes or environmental factors, maintaining accuracy over the device lifetime.
4Measurement precision
If individual sensor calibration is performed for each unit, then measurement precision is improved, but manufacturing cost and time increase
Solution Approach 1:
The accelerometer performs self-calibration in the field without requiring factory calibration equipment or processes. Each device calibrates itself automatically during normal usage, eliminating the need for time-consuming individual calibration steps on the production line and reducing manufacturing costs while maintaining per-unit calibration accuracy.
Data Source
AI summary
A method, system and computer readable medium for calibrating an accelerometer in a portable device is disclosed. The method, system and computer readable medium comprises receiving data from the accelerometer, and providing accelerometer samples from the data based upon one or more selection rules that adaptively selects data that satisfy certain criteria. The method system and computer readable medium also includes fitting the accelerometer samples to a mathematical mode. The method system and computer readable medium further includes providing a bias of the accelerometer based upon a center of the mathematical model.