CALIBRATING MULTI-DIMENSIONAL SENSOR FOR OFFSET, SENSITIVITY, AND NON-ORTHOGONALITY

• US 20110106477A1
• Filed: 11/04/2009
• Published: 05/05/2011
• Est. Priority Date: 11/04/2009
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Brunner, Christopher
• Granted Patent: US 8,645,093 B2
• Time in Patent Office: Days
• US Class Current: 702/104
• CPC Class Codes: G01R 33/0035 Calibration of single magnetic sensors, e.g. integrated calibration
• CPC Class Codes: G01R 33/0206 Three-component magnetometers
• CPC Class Codes: G06F 3/0346 with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
• CPC Class Codes: G06F 3/038 Control and interface arrangements therefor, e.g. drivers or device-embedded control circuitry

Abstract

A multi-dimensional sensor, a magnetometer or accelerometer, is calibrated based on the raw data provided by the sensor. Raw data is collected and may be used to generate ellipse or ellipsoid parameters, for a two-dimensional or three-dimensional sensor, respectively. An offset calibration factor is calculated based on the raw data, e.g., the determined ellipse or ellipsoid parameters. A sensitivity calibration factor is then calculated based on the offset calibration factor and the raw data. A non-orthogonality calibration factor can then be calculated based on the calculated offset and sensitivity calibration factors. Using the offset, sensitivity and non-orthogonality calibration factors, the raw data can be corrected to produce calibrated data.

Claims

1. A method comprising:

collecting raw data for a plurality of axes of a multi-dimensional sensor and providing the collected raw data to a processor;

calculating with the processor an offset calibration factor for each of the plurality of axes of the multi-dimensional sensor based on the raw data;

calculating with the processor a sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor based on the calculated offset calibration factor and the raw data;

calculating with the processor a non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor based on the calculated offset calibration factor, the calculated sensitivity calibration factor and the raw data;

storing the calculated offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factor in memory coupled to the processor; and

correcting raw data for the plurality of axes of the multi-dimensional sensor with the processor using the stored calculated offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factor.

2. The method of claim 1, wherein the multi-dimensional sensor is a magnetometer and the raw data is collected outside the presence of a disturbing magnetic near field.

3. The method of claim 1, wherein the multi-dimensional sensor is an accelerometer and the raw data is collected while the accelerometer is subjected to no acceleration beyond gravity.

4. The method of claim 1, wherein:

the offset calibration factor for each of the plurality of axes of the multi-dimensional sensor compensates for at least a DC offset along each axis of the multi-dimensional sensor;

the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor compensates for at least a sensitivity of each axis of the multi-dimensional sensor, the sensitivity of each axis being dissimilar; and

the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor compensates for misalignment between one or more pairs of axes of the multi-dimensional sensor.

5. The method of claim 1, further comprising determining at least one of ellipse parameters and ellipsoid parameters are using the raw data, wherein the ellipse parameters are A, B, C, D, E, and L and are defined by:

AB_x²+BB_y²+2CB_xB_y+2DB_x+2EB_y+L=0

where B_x and B_y the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the ellipsoid parameters are A, B, C, D, E, F, G, H, K and L and are defined by;

AB_x²+BB_y²+CB_z²+2DB_xB_y+2EB_xB_z+2FB_yB_z+2GB_x+2HB_y+2KB_zL=0

where B_x, B_y, and B_z are the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factors are calculated based on the at least one of ellipse parameter and ellipsoid parameters.

6. The method of claim 5, wherein ellipsoid parameters are determined, the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor is calculated as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

7. The method of claim 6, wherein the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor is calculated as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

θ

=Q(1,3)/2;

φ

=Q(2,3)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane, θ

denotes non-orthogonality in radians in the x-z plane; and

0 denotes non-orthogonality in radians in the y-z plane.

8. The method of claim 7, wherein the raw data for the plurality of axes of the multi-dimensional sensor are corrected as follows:

9. The method of claim 5, wherein ellipse parameters are determined and wherein the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor is calculated as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

10. The method of claim 9, wherein the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor is calculated as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane.

11. The method of claim 10, wherein the raw data for the plurality of axes of the multi-dimensional sensor are corrected as follows:

12. A mobile station comprising:

a multi-dimensional sensor that provides raw data for each of a plurality of axes;

a processor connected to the multi-dimensional the processor receives from the multi-dimensional sensor the raw data for each of the plurality of axes;

memory connected to the processor; and

software held in the memory and run in the processor to calibrate the multi-dimensional sensor, the software including instructions to calculate and store in memory an offset calibration factor for each of the plurality of axes based on the raw data;

to calculate and store in memory a sensitivity calibration factor for each of the plurality of axes based on the calculated offset calibration factor and the raw data; and

to calculate and store in memory a non-orthogonality calibration factor for one or more pairs of axes based on the calculated offset calibration factor, the calculated sensitivity calibration factor and the raw data;

the software further including instructions to correct the raw data for each of the plurality of axes using the stored calculated offset calibration factors, the stored sensitivity calibration factors and the stored one or more non-orthogonality calibration factor.

13. The mobile station of claim 12, wherein the multi-dimensional sensor is at least one of a magnetometer and an accelerometer.

14. The mobile station of claim 12, wherein:

the offset calibration factor for each of the plurality of axes of the multi-dimensional sensor compensates for at least a DC offset along each axis of the multi-dimensional sensor;

the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor compensates for at least a sensitivity of each axis of the multi-dimensional sensor, the sensitivity of each axis being dissimilar; and

the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor compensates for misalignment between the one or more pairs of axes of the multi-dimensional sensor.

15. The mobile station of claim 12, the software further including instructions to determine at least one of ellipse parameters and ellipsoid parameters are using the raw data, wherein the ellipse parameters are A, B, C, D, E, and L and are defined by:

AB_x²+BB_y²+2CB_xB_y+2DB_x+2EB_y+L=0

where B_x and B_y the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the ellipsoid parameters are A, B, C, D, E, F, G, H, K and L and are defined by;

AB_x²+BB_y²+CB_z²+2DB_xB_y+2EB_xB_z+2FB_yB_z+2GB_x+2HB_y+2KB_z+L=0

where B_x, B_y, and B_z are the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factors are calculated based on the at least one of ellipse parameter and ellipsoid parameters.

16. The mobile station of claim 15, wherein the software includes instructions to calculate the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

17. The mobile station of claim 16, wherein software includes instructions to calculate the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

θ

=Q(1,3)/2;

φ

=Q(2,3)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane, θ

denotes non-orthogonality in radians in the x-z plane; and

φ

denotes non-orthogonality in radians in the y-z plane.

18. The mobile station of claim 17, wherein the software includes instructions to correct the raw data for each of the plurality of axes using the stored calculated offset calibration factors, the stored sensitivity calibration factors and the stored one or more non-orthogonality calibration factor as follows:

19. The mobile station of claim 15, wherein the software includes instructions to calculate the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

20. The mobile station of claim 19, wherein software includes instructions to calculate the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor as:

Q=diag(1./scale)*pinv(Amat/C_dash) * diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane.

21. The mobile station of claim 20, wherein the software includes instructions to correct the raw data for each of the plurality of axes using the stored calculated offset calibration factors, the stored sensitivity calibration factors and the stored one or more non-orthogonality calibration factor as follows:

22. A mobile station comprising:

means for collecting raw data for a plurality of axes of a multi-dimensional sensor;

means for calculating an offset calibration factor for each of the plurality of axes of the multi-dimensional sensor based on the raw data;

means for calculating a sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor based on the calculated offset calibration factor and the raw data;

means for calculating a non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor based on the calculated offset calibration factor, the calculated sensitivity calibration factor and the raw data; and

means for correcting raw data for the plurality of axes of the multi-dimensional sensor using the calculated offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factors.

23. The mobile station of claim 22, wherein the multi-dimensional sensor is at least one of a magnetometer and an accelerometer.

24. The mobile station of claim 22, wherein:

the offset calibration factor for each of the plurality of axes of the multi-dimensional sensor compensates for at least a DC offset along each axis of the multi-dimensional sensor;

the sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor compensates for at least a sensitivity of each axis of the multi-dimensional sensor, the sensitivity of each axis being dissimilar; and

the non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor compensates for misalignment between the one or more pairs of axes of the multi-dimensional sensor.

25. The mobile station of claim 22, further comprising means for determining at least one of ellipse parameters and ellipsoid parameters are using the raw data, wherein the ellipse parameters are A, B, C, D, E, and L and are defined by:

AB_x²+BB_y²+2CB_xB_y+2DB_x+2EB_y+L=0

where B_x and B_y the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the ellipsoid parameters are A, B, C, D, E, F, G, H, K and L and are defined by;

AB_x²+BB_y²+CB_z²+2DB_xB_y+2EB_xB_z+2FB_yB_z+2GB_x+2HB_y+2KB_z+L=0

where B_x, B_y, and B_z are the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factors are calculated based on the at least one of ellipse parameter and ellipsoid parameters.

26. The mobile station of claim 25, wherein the means for calculating the sensitivity calibration factor calculates the sensitivity calibration factor as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

27. The mobile station of claim 26, wherein the means for calculating the non-orthogonality calibration factor calculates the non-orthogonality factor as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

θ

=Q(1,3)/2;

φ

=Q(2,3)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane, θ

denotes non-orthogonality in radians in the x-z plane; and

₉5 denotes non-orthogonality in radians in the y-z plane.

28. The mobile station of claim 26, wherein the means for correcting the raw data corrects the raw data as:

29. The mobile station of claim 25, wherein the means for calculating the sensitivity calibration factor calculates the sensitivity calibration factor as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

30. The mobile station of claim 29, wherein the means for calculating the non-orthogonality calibration factor calculates the non-orthogonality factor as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane.

31. The mobile station of claim 30, wherein the means for correcting the raw data corrects the raw data as:

32. A computer-readable medium including program code stored thereon, comprising:

program code to calculate an offset calibration factor for each of a plurality of axes of a multi-dimensional sensor based on collected raw data for the plurality of axes of the multi-dimensional sensor;

program code to calculate a sensitivity calibration factor for each of the plurality of axes of the multi-dimensional sensor based on the calculated offset calibration factor and the collected raw data for the plurality of axes of the multi-dimensional sensor;

program code to calculate a non-orthogonality calibration factor for one or more pairs of axes of the multi-dimensional sensor based on the calculated offset calibration factor, the calculated sensitivity calibration factor and the collected raw data for the plurality of axes of the multi-dimensional sensor; and

program code to correct raw data for the plurality of axes of the multi-dimensional sensor using the calculated offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factors.

33. The computer-readable medium of claim 32, wherein the program code further comprises program code to determine at least one of ellipse parameters and ellipsoid parameters are using the raw data, wherein the ellipse parameters are A, B, C, D, E, and L and are defined by:

AB_x²+BB_y²+2CB_xB_y+2DB_x+2EB_y+L=0

where B_x, and B_y the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the ellipsoid parameters are A, B, C, D, E, F, G, H, K and L and are defined by;

AB_x²+BB_y²+CB_z²+2DB_xB_y+2EB_xB_z+2FB_yB_z+2GB_x+2HB_y+2KB_z+L=0

where B_x, B_y, and B_z are the raw data measured along the plurality of axes of the multi-dimensional sensor, and wherein the offset calibration factors, the sensitivity calibration factors and one or more non-orthogonality calibration factors are calculated based on the at least one of ellipse parameter and ellipsoid parameters.

34. The computer-readable medium of claim 33, wherein the program code to calculate the sensitivity calibration factor calculates the sensitivity calibration factor as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

35. The computer-readable medium of claim 34, wherein the program code to calculate the non-orthogonality calibration factor calculates the non-orthogonality factor as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

θ

=Q(1,3)/2;

φ

=Q(2,3)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane, θ

denotes non-orthogonality in radians in the x-z plane; and

φ

denotes non-orthogonality in radians in the y-z plane.

36. The computer-readable medium of claim 35, wherein the program code to correct the raw data corrects the raw data as:

37. The computer-readable medium of claim 33, wherein the program code to calculate the sensitivity calibration factor calculates the sensitivity calibration factor as:

scale=√

{square root over (diag(pinv(Amat/c_dash)))}

where scale is the sensitivity calibration factor, diag denotes the diagonal components of a matrix, pinv denotes pseudoinverse, and

38. The computer-readable medium of claim 37, wherein the program code to calculate the non-orthogonality calibration factor calculates the non-orthogonality factor as:

Q=diag(1./scale)*pinv(Amat/C_dash)*diag(1./scale)

where Q is a matrix, scale is a vector with length 3, 1./scale indicates taking the reciprocal of each element, and diag() indicates turning a vector into a matrix of zeros except for the diagonal, which correspond to the vector; and

ψ

=Q(1,2)/2;

where ψ

denotes non-orthogonality in radians in the x-y plane.

39. The computer-readable medium of claim 38, wherein the program code to correct the raw data corrects the raw data as: