Magnetometer Accelerometer Data Fusion for Attitude Heading
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Solution Overview
Problem
Implementing a three-axis magnetometer and a three-axis accelerometer in mobile devices is challenging due to sensor error sources like bias, scale factor misalignment, and distortions from hard and soft iron interference, which affect accurate attitude and heading determination, and existing factory calibration methods are not commercially viable for consumer applications.
Innovation Solution
The method involves data fusion of three-axis magnetometer and accelerometer data, with on-line calibration to correct for sensor errors, filter out noise, and compensate for hard and soft iron distortions, using transformation matrices and Kalman filters to calculate accurate angular rates and orientation, enabling autonomous calibration without requiring expensive factory calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory calibration is used to correct sensor errors, then measurement precision is improved, but device cost increases significantly
Solution Approach 1:
The system performs autonomous self-calibration using the accelerometer and magnetometer sensors themselves to detect and correct hard and soft iron distortions. The calibration process uses the device's own motion data and magnetic field measurements to automatically compute correction parameters without external equipment or factory intervention, enabling the device to calibrate itself during normal operation.
Solution Approach 2:
The system dynamically adjusts calibration parameters (bias, scale factors, and distortion coefficients) based on environmental conditions and device orientation. By continuously monitoring sensor outputs and detecting patterns indicative of hard and soft iron effects, the system adapts correction parameters in real-time to maintain accuracy without requiring fixed factory calibration values.
2Measurement precision
If sophisticated calibration procedures are implemented, then sensor error correction is improved, but device complexity increases
Solution Approach 1:
The calibration functions are merged with the normal operational code of the mobile device. The same processor that runs the operating system and applications also performs calibration computations using existing sensor data. The calibration algorithm is integrated into the device's firmware, sharing computational resources and code pathways with other device functions, thereby avoiding the need for separate dedicated calibration hardware or processing systems.
3Measurement precision
If on-line calibration is performed continuously, then measurement accuracy is maintained, but energy consumption increases
Solution Approach 1:
The system performs calibration computations periodically rather than continuously, triggering calibration routines based on detected events such as device motion patterns, orientation changes, or time intervals. The processor enters low-power states between calibration events, significantly reducing energy consumption while maintaining accuracy by recalibrating only when necessary to account for environmental changes or device movement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate and reliable attitude and heading information, effectively removing sensor errors and distortions, allowing for precise orientation and heading determination in mobile devices, even in the presence of hard and soft iron interference, while reducing costs by eliminating the need for sophisticated factory calibration.
Implementation Method 1
three-axis accelerometer data
Implementation Method 2
three-axis magnetometer data
Data Source
AI summary
Method and apparatus of integrating a three-axis magnetometer and a three-axis accelerometer to provide attitude and heading, calibrated magnetometer and accelerometer data, and angular rate, while removing sensor error sources over time and temperature and to compensate for hard and soft iron distortions of the Earth magnetic field. Filtered accelerometer data are corrected to account for various error sources. The magnetic heading is calculated from a horizontal magnetic field vector transformed from three dimensional Earth's magnetic field vector by using quasi-static roll and pitch angles from the filtered accelerometer data. A first Kalman filter estimates the state vector, based on the principle that the magnitude of local Earth's magnetic field vector is constant, to form hard and soft iron correction matrices. A second Kalman filter estimates a correction matrix of coupled remaining soft iron and the misalignment of the magnetometer and the accelerometer, as the dot product of local Earth's magnetic field vector and a corrected gravitational acceleration vector, at a quasi-static position, is constant. The three dimensional Earth's magnetic field vector is received by removing the hard and soft irons through the soft iron and hard iron correction matrixes.