Magnetometer calibration

• US 8,717,009 B2
• Filed: 10/06/2010
• Issued: 05/06/2014
• Est. Priority Date: 10/06/2010
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Tu, Xiaoyuan
• Primary Examiner(s): LEDYNH, BOT L
• Application Number:
  US12/899,392
• Publication Number:
  US 20120086438A1
• Time in Patent Office: 1,308 Days
• Field of Search: 324/202, 701/480
• US Class Current: 324/202
• CPC Class Codes: G01R 33/0035 Calibration of single magnetic sensors, e.g. integrated calibration
• CPC Class Codes: G01R 33/028 Electrodynamic magnetometers
• CPC Class Codes: G01R 35/00 Testing or calibrating of apparatus covered by the other groups of this subclass

Abstract

A real-time calibration system and method for a mobile device having an onboard magnetometer uses an estimator to estimate magnetometer calibration parameters and a magnetic field external to the mobile device (e.g., the earth magnetic field). The calibration parameters can be used to calibrate uncalibrated magnetometer readings output from the onboard magnetometer. The external magnetic field can be modeled as a weighted combination of a past estimate of the external magnetic field and the asymptotic mean of that magnetic field, perturbed by a random noise (e.g., Gaussian random noise). The weight can be adjusted based on a measure of the statistical uncertainty of the estimated calibration parameters and the estimated external magnetic field. The asymptotic mean of the external magnetic field can be modeled as a time average of the estimated external magnetic field. are within the scope of the following claims.

Claims

1. A computer-implemented method performed by a mobile device having a magnetometer, comprising:

receiving uncalibrated magnetometer readings;

receiving orientation data indicating a current orientation of the mobile device;

estimating calibration parameters for the magnetometer readings based on the uncalibrated magnetometer readings, the orientation data and an estimate of a magnetic field external to the mobile device, where the external magnetic field is estimated by calculating a weighted combination of a past estimate of the external magnetic field and an asymptotic mean of the external magnetic field, perturbed by random noise; and

calibrating the uncalibrated magnetometer readings using the estimated calibration parameters.

2. The method of claim 1, further comprises:

generating the orientation data from measurements output by an angular rate sensor onboard the mobile device.

3. The method of claim 2, where estimating calibration parameters further comprises:

estimating bias and scale factor errors from the measurements.

4. The method of claim 3, further comprising:

estimating the bias, scale factor and external magnetic field parameters using a recursive estimator.

5. The method of claim 4, where the recursive estimator is a Kalman Filter.

6. The method of claim 1, where the calculating further comprises:

weighting the combination with a dynamically adjustable tuning factor.

7. The method of claim 6, where the external magnetic field is given by

{right arrow over (m)}_t=α

m<sub>t−

1</sub>+(1−

α

){right arrow over (e)}+√

{square root over (1−

α

²)}w<sub>t−

1</sub>,

where {right arrow over (m)}_t is an external magnetic vector at time t, {right arrow over (m)}<sub>t−

1</sub> is an external magnetic vector at time t−

1, α

a is the dynamically adjusted tuning parameter, {right arrow over (e)} is an estimate of the asymptotic mean of the external magnetic field and w<sub>t−

1</sub> a random variable with zero mean and covariance σ

.

8. The method of claim 7, further comprising:

dynamically adjusting the tuning parameter based on an uncertainty in the estimate of the calibration parameters and the external magnetic field.

9. The method of claim 8,

modeling the estimate of the asymptotic mean of the external magnetic field as a time average of the external magnetic field.

10. The method of claim 9, where the estimate of the asymptotic mean of the external magnetic field is given by

11. A magnetometer calibration system, comprising:

a magnetometer;

an angular rate sensor;

a processor coupled to the magnetometer and angular rate sensor, the processor configured for executing instructions to perform operations comprising;

receiving uncalibrated magnetometer readings from the magnetometer;

receiving orientation data from the angular rate sensor indicating a current orientation of the mobile device;

estimating calibration parameters for the magnetometer readings based on the uncalibrated magnetometer readings, the orientation data and an estimate of a magnetic field external to the mobile device, where the external magnetic field is estimated by calculating a weighted combination of a past estimate of the external magnetic field and an asymptotic mean of the external magnetic field, perturbed by random noise; and

calibrating the uncalibrated magnetometer readings using the estimated calibration parameters.

12. The system of claim 11, where estimating calibration parameters further comprises estimating bias and scale factor errors from the measurements.

13. The system of claim 12, where the bias, scale factor and external magnetic field parameters are estimated using a recursive estimator.

14. The system of claim 13, where the recursive estimator is a Kalman Filter.

15. The system of claim 11, where the combination is weighted with a dynamically adjustable tuning factor.

16. The system of claim 15, where the external magnetic field is modeled by

{right arrow over (m)}_t=α

m<sub>t−

1</sub>+(1−

α

){right arrow over (e)}+√

{square root over (1−

α

²)}w<sub>t−

1</sub>,

where {right arrow over (m)}_t is an external magnetic vector at time t, {right arrow over (m)}<sub>t−

1</sub> is an external magnetic vector at time t−

1, α

is the dynamically adjusted tuning parameter, {right arrow over (e)} is an estimate of the asymptotic mean of the external magnetic field and w<sub>t−

1</sub>a random variable with zero mean and covariance σ

.

17. The system of claim 16, where the tuning parameter is dynamically adjusted based on an uncertainty in the estimate of the calibration parameters and the external magnetic field.

18. The system of claim 17, where the estimate of the asymptotic mean of the external magnetic field is modeled as a time average of the external magnetic field.

19. The system of claim 18, where the estimate of the asymptotic mean of the external magnetic field is given by