Magnetometer calibration

• US 9,229,084 B2
• Filed: 05/05/2014
• Issued: 01/05/2016
• 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
• Application Number:
  US14/269,677
• Publication Number:
  US 20140244200A1
• Time in Patent Office: 610 Days
• Field of Search: 324/202, 701/480
• US Class Current: 1/1
• 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.

Claims

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

receiving uncalibrated magnetometer readings from the magnetometer;

estimating, by the mobile device, bias calibration parameters based on the uncalibrated magnetometer readings and an algebraic linearization of a sphere constraint model using a bias vector and an intermediate variable;

calibrating, by the mobile device, the uncalibrated magnetometer readings using the estimated bias calibration parameters;

generating, by the mobile device, a compass heading from the calibrated magnetometer readings; and

presenting, by the mobile device, the compass heading.

2. The method of claim 1, where the estimating further comprises

estimating using a linear Kalman Filter formulation, whose state vector includes both the bias vector and the intermediate variable.

3. The method of claim 2, further comprising:

modeling time dynamics of the intermediate variable using a Gauss-Markov process.

4. The method of claim 3, further comprising:

modeling the intermediate variable μ

by

μ

_t=β

μ

_t-1+(1−

β

)ρ

+√

{square root over ((1−

β

²))}υ

_t-1,

where β

is a weighting factor valued in [0 1], ρ

is an estimate of the asymptotic mean of μ and

υ

is a random variable with a certain given variance and zero mean.

5. A magnetometer calibration system, comprising:

a magnetometer;

a processor coupled to the magnetometer and configured for executing instructions to perform operations comprising;

receiving uncalibrated magnetometer readings;

estimating bias calibration parameters based on the uncalibrated magnetometer readings and an algebraic linearization of a sphere constraint model using a bias vector and an intermediate variable;

calibrating the uncalibrated magnetometer readings using the estimated bias calibration parameters;

generating, by the mobile device, a compass heading from the calibrated magnetometer readings; and

presenting, by the mobile device, the compass heading.

6. The system of claim 5, where a linear Kalman Filter formulation is used to estimate a state vector that includes the bias vector and the intermediate variable.

7. The system of claim 6, where the time dynamics of the intermediate variable is modeled using a Gauss-Markov process.

8. The system of claim 7, where the intermediate variable p is modeled by

μ

_t=β

μ

_t-1+(1−

β

)ρ

+√

{square root over ((1−

β

²))}υ

_t-1,

where β

is a weighting factor valued in [0 1], ρ

is an estimate of the asymptotic mean of μ and

υ

is a random variable with a certain given variance and zero mean.