SYSTEMS AND METHODS FOR SENSOR CALIBRATION

• US 20150285835A1
• Filed: 04/07/2014
• Published: 10/08/2015
• Est. Priority Date: 12/23/2013
• Status: Abandoned Application

• Current Assignee: Invensense Incorporated (TDK Corporation)
• Original Assignee: Invensense Incorporated (TDK Corporation)
• Inventors: Karahan, Sinan, Lin, Shang-Hung, Morozov, Alexey
• Application Number:
  US14/247,142
• Publication Number:
  US 20150285835A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 25/005 initial alignment, calibration or starting-up of inertial devices
• CPC Class Codes: G01P 21/00 Testing or calibrating of apparatus or devices covered by the preceding groups
• CPC Class Codes: G01R 35/005 Calibrating; Standards or reference devices, e.g. voltage or resistance standards, "golden" references G01R33/0035, G01R35/002 take precedence

Abstract

Systems and methods are disclosed for calibrating a sensor. A recursive least squares estimation may be performed to update a mean and a covariance matrix for samples of data from a motion sensor and a bias estimate for the motion sensor may be derived from the mean and covariance matrix. The motion sensor may be an gyroscope, an accelerometer or a magnetometer.

Claims

25. A sensor device comprising;

at least one motion sensor outputting a plurality of data samples; and

a calibration module configured to perform a recursive least squares estimation to update a mean and a covariance matrix for each of the plurality of data samples and derive a bias estimate for the motion sensor from the mean and covariance matrix.

1. A method for calibrating a motion sensor comprising:

obtaining a plurality of data samples from the motion sensor;

performing a recursive least squares estimation to update a mean and a covariance matrix for each of the plurality of data samples; and

deriving a bias estimate for the motion sensor from the mean and covariance matrix.

2. The method of claim 1, wherein the motion sensor is a gyroscope.

3. The method of claim 2, wherein each of the plurality of data samples comprises a measured angular rate, further comprising:

obtaining a corrected attitude for each sample from sensor fusion data;

determining an angular rate corresponding to the corrected attitude; and

performing the recursive least squares estimation using each measured angular rate and each determined angular rate to derive the bias.

4. The method of claim 1, wherein the motion sensor is an accelerometer.

5. The method of claim 1, further comprising determining a standard deviation using the plurality of data samples and performing the least square estimation for a first data sample depending on a comparison to the standard deviation.

6. The method of claim 5, further comprising determining a covariance value and an innovation value with respect to the first data sample and deriving the bias using the first data sample depending on a comparison to the covariance value and the innovation value.

7. The method of claim 4, further comprising:

rotating each of the plurality of data samples to a world coordinate frame;

determining a gravity vector for each of the rotated plurality of data samples;

setting the determined gravity vectors equal; and

performing the recursive least squares estimation to derive the bias.

8. The method of claim 4, further comprising:

fitting the plurality of data samples to a sphere having a radius equal to a gravitational constant; and

performing the recursive least squares estimation to determine a center of the sphere by computing Cartesian coordinates of the plurality of data samples, wherein the center corresponds to the bias.

9. The method of claim 8, wherein computing Cartesian coordinates of the plurality of data samples comprises grouping non-linear terms as an unknown in the recursive least squares estimation.

10. The method of claim 4, further comprising:

fitting the plurality of data samples to a sphere having a radius equal to a gravitational constant;

generating a first vector from a pair of data samples of the plurality of data samples,

generating a second vector from another pair of data samples of the plurality of data samples; and

performing the recursive least squares estimation to determine a center of the sphere by computing an intersection of perpendiculars of the first vector and the second vector, wherein the center corresponds to the bias.

11. The method of claim 4, further comprising:

fitting the plurality of data samples to a sphere having a radius equal to a gravitational constant; and

performing the recursive least squares estimation to determine a center of the sphere by subtracting a first data sample of the plurality of data samples taken at a first time from a second data sample of the plurality of data samples taken at a second time, wherein the center corresponds to the bias.

12. The method of claim 1, wherein the motion sensor is a magnetometer.

13. The method of claim 12, further comprising determining an innovation vector corresponding to the recursive least squares estimation and detecting a magnetic anomaly based at least in part on the innovation vector.

14. The method of claim 12, further comprising:

determining a standard deviation using the plurality of data samples;

determining an absolute value of the difference between a first data sample and the derived bias; and

performing the recursive least square estimate with respect to the first data sample depending on a comparison to the standard deviation and the absolute value.

15. The method of claim 14, further comprising:

determining an absolute value of a difference between a reference radius and a distance with respect to the derived bias and the first data sample;

determining a maximum diagonal covariance value with respect to the first sample; and

deriving the bias using the first data sample depending on a comparison to the absolute value and the maximum diagonal covariance value.

16. The method of claim 12, further comprising:

rotating each of the plurality of data samples to a world coordinate frame;

determining a reference vector corresponding to the Earth'"'"'s magnetic field for each of the rotated plurality of data samples;

setting the determined reference vectors equal; and

performing the recursive least squares estimation to derive the bias.

17. The method of claim 12, further comprising:

fitting the plurality of data samples to a sphere having a radius equal to a magnitude of the Earth'"'"'s magnetic field;

performing the recursive least squares estimation to determine a center of the sphere by computing Cartesian coordinates of the plurality of data samples, wherein the center corresponds to the bias.

18. The method of claim 17, wherein computing Cartesian coordinates of the plurality of data samples comprises grouping non-linear terms as an unknown in the recursive least squares estimation.

19. The method of claim 12, further comprising:

fitting the plurality of data samples to a sphere having a radius equal to a magnitude of the Earth'"'"'s magnetic field;

generating a first vector from a pair of data samples of the plurality of data samples,

generating a second vector from another pair of data samples of the plurality of data samples;

performing the recursive least squares estimation to determine a center of the sphere by computing an intersection of perpendiculars of the first vector and the second vector, wherein the center corresponds to the bias.

20. The method of claim 14, further comprising:

fitting the plurality of data samples to a sphere having a radius equal to a magnitude of the Earth'"'"'s magnetic field;

performing the recursive least squares estimation to determine a center of the sphere by subtracting a first data sample of the plurality of data samples taken at a first time from a second data sample of the plurality of data samples taken at a second time, wherein the center corresponds to the bias.

21. The method of claim 1, wherein the sensor is calibrated on a single axis.

22. The method of claim 1, further comprising sensing a temperature of the motion sensor and providing a temperature compensation to the calibration based at least in part on the sensed temperature.

23. The method of claim 22, wherein the temperature compensation is determined using a recursive least squares estimation.

24. The method of claim 1, further comprising determining a confidence metric based at least in part on the covariance matrix.

26. The sensor device of claim 25, wherein the motion sensor is a gyroscope.

27. The sensor device of claim 26, wherein each of the plurality of data samples comprises a measured angular rate and wherein the calibration module is configured to:

obtain a corrected attitude for each sample from sensor fusion data;

determine an angular rate corresponding to the corrected attitude; and

perform the recursive least squares estimation using each measured angular rate and each determined angular rate to derive the bias.

28. The sensor device of claim 25, wherein the motion sensor is an accelerometer.

29. The sensor device of claim 25, wherein the calibration module is configured to determine a standard deviation using the plurality of data samples and perform the least square estimation for a first data sample depending on a comparison to the standard deviation.

30. The sensor device of claim 29, wherein the calibration module is configured to determine a covariance value and an innovation value with respect to the first data sample and derive the bias using the first data sample depending on a comparison to the covariance value and the innovation value.

31. The sensor device of claim 28, wherein the calibration module is configured to:

rotate each of the plurality of data samples to a world coordinate frame;

determine a gravity vector for each of the rotated plurality of data samples;

set the determined gravity vectors equal; and

perform the recursive least squares estimation to derive the bias.

32. The sensor device of claim 28, wherein the calibration module is configured to:

fit the plurality of data samples to a sphere having a radius equal to a gravitational constant; and

perform the recursive least squares estimation to determine a center of the sphere by computing Cartesian coordinates of the plurality of data samples, wherein the center corresponds to the bias.

33. The sensor device of claim 32, wherein computing Cartesian coordinates of the plurality of data samples comprises grouping non-linear terms as an unknown in the recursive least squares estimation.

34. The sensor device of claim 28, wherein the calibration module is configured to:

fit the plurality of data samples to a sphere having a radius equal to a gravitational constant;

generate a first vector from a pair of data samples of the plurality of data samples,

generate a second vector from another pair of data samples of the plurality of data samples;

perform the recursive least squares estimation to determine a center of the sphere by computing an intersection of perpendiculars of the first vector and the second vector, wherein the center corresponds to the bias.

35. The sensor device of claim 28, wherein the calibration module is configured to:

fit the plurality of data samples to a sphere having a radius equal to a gravitational constant; and

perform the recursive least squares estimation to determine a center of the sphere by subtracting a first data sample of the plurality of data samples taken at a first time from a second data sample of the plurality of data samples taken at a second time, wherein the center corresponds to the bias.

36. The sensor device of claim 25, wherein the motion sensor is a magnetometer.

37. The sensor device of claim 36, wherein the calibration module is configured to determine an innovation vector corresponding to the recursive least squares estimation and detect a magnetic anomaly based at least in part on the innovation vector.

38. The sensor device of claim 36, wherein the calibration module is configured to:

determine a standard deviation using the plurality of data samples;

determine an absolute value of the difference between a first data sample and the derived bias; and

perform the recursive least square estimate with respect to the first data sample depending on a comparison to the standard deviation and the absolute value.

39. The sensor device of claim 38, wherein the calibration module is configured to:

determine an absolute value of a difference between a reference radius and a distance with respect to the derived bias and the first data sample;

determine a maximum diagonal covariance value with respect to the first sample; and

derive the bias using the first data sample depending on a comparison to the absolute value and the maximum diagonal covariance value.

40. The sensor device of claim 36, wherein the calibration module is configured to:

rotate each of the plurality of data samples to a world coordinate frame;

determine a reference vector corresponding to the Earth'"'"'s magnetic field for each of the rotated plurality of data samples;

set the determined reference vectors equal; and

perform the recursive least squares estimation to derive the bias.

41. The sensor device of claim 36, wherein the calibration module is configured to:

fit the plurality of data samples to a sphere having a radius equal to a magnitude of the Earth'"'"'s magnetic field; and

perform the recursive least squares estimation to determine a center of the sphere by computing Cartesian coordinates of the plurality of data samples, wherein the center corresponds to the bias.

42. The sensor device of claim 41, wherein computing Cartesian coordinates of the plurality of data samples comprises grouping non-linear terms as an unknown in the recursive least squares estimation.

43. The sensor device of claim 36, wherein the calibration module is configured to:

fit the plurality of data samples to a sphere having a radius equal to a magnitude of the Earth'"'"'s magnetic field; and

perform the recursive least squares estimation to determine a center of the sphere by subtracting a first data sample of the plurality of data samples taken at a first time from a second data sample of the plurality of data samples taken at a second time, wherein the center corresponds to the bias.

44. The sensor device of claim 25, wherein the sensor is calibrated on a single axis.

45. The sensor device of claim 25, further comprising a temperature sensor, wherein the calibration module is configured to provide temperature compensation to the calibration based at least in part on the sensed temperature.

46. The sensor device of claim 45, wherein the calibration module is configured to determine the temperature compensation using a recursive least squares estimation.

47. The sensor device of claim 25, wherein the calibration module is configured to determine a confidence metric based at least in part on the covariance matrix.

48. A self-calibrating sensor device comprising at least one motion sensor outputting a plurality of data samples and a calibration module configured to derive a bias estimate for the motion sensor, wherein the at least one motion sensor and the calibration module are implemented on a single substrate.