Real-Time Dynamic Tracking of Bias

• US 20090033807A1
• Filed: 06/27/2008
• Published: 02/05/2009
• Est. Priority Date: 06/28/2007
• Status: Active Grant

• Current Assignee: DRNC Holdings, Inc. (InterDigital, Inc.)
• Original Assignee: Hillcrest Laboratories Incorporated (CEVA Incorporated)
• Inventors: Sheng, Hua, Liberty, Matthew G., Roller, Christopher D., Gritton, Charles W.K.
• Granted Patent: US 7,860,676 B2
• Time in Patent Office: Days
• US Class Current: 348/734
• CPC Class Codes: G01D 3/028 mitigating undesired influences, e.g. temperature, pressure
• CPC Class Codes: G01R 29/00 Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
• CPC Class Codes: G06F 3/033 Pointing devices displaced or positioned by the user , e.g. mice, trackballs, pens or joysticks; Accessories therefor digitisers characterised by the transducing means G06F3/041
• 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: H04N 21/42204 User interfaces specially adapted for controlling a client device through a remote control device; Remote control devices therefor constructive details of casings for the remote control device H01H9/0235; user interfaces for controlling a tuning device of a television receiver through a remote control H03J9/00; remote control of peripheral devices connected to a television receiver through the remote control device of the television receiver H04B1/205
• CPC Class Codes: H04N 21/42222 Additional components integrated in the remote control device, e.g. timer, speaker, sensors for detecting position, direction or movement of the remote control, microphone or battery charging device

Abstract

A bias value associated with a sensor, e.g., a time-varying, non-zero value which is output from a sensor when it is motionless, is estimated using at least two, different bias estimating techniques. A resultant combined or selected bias estimate may then be used to compensate the biased output of the sensor in, e.g., a 3D pointing device.

Claims

1. A device comprising:

at least one sensor for sensing rotation of said device about a first axis and generating at least one first output associated therewith and for sensing rotation of said device about a second axis and generating at least one second output associated therewith;

an accelerometer for sensing an acceleration of said device and outputting at least one third output associated therewith;

a temperature sensor for sensing a temperature of said device and outputting at least one temperature output associated therewith; and

a processing unit for determining a bias estimate associated with said at least one sensor using;

(a) a first bias estimate technique for generating first bias estimate data based upon determining whether said device is stationary using said at least one first, second and third outputs;

(b) a second bias estimate technique for generating second bias estimate data based upon detection of a pitch of said device using said at least one first, second and third outputs;

(c) a third bias estimate technique for generating third bias estimate data based upon slew-rate filtering using said at least one first and second outputs; and

(d) a fourth bias estimate technique for generating fourth bias estimate data based upon said temperature output and said at least one first and second outputs.

2. The device of claim 1, wherein said sensed rotation of said device involves at least one of:

an angular velocity, an angular rate, and an angular position of said device.

3. The device of claim 1, wherein said bias estimate is associated with a non-zero value output from said at least one sensor when said device is stationary.

4. A method for estimating bias associated with a sensor comprising:

estimating said bias associated with said sensor using at least a first bias estimating technique to generate a first bias estimate and a second bias estimating technique to generate a second bias estimate.

5. The method of claim 4, wherein said first bias estimate and said second bias estimate are associated with a non-zero value output from said sensor when an object to which said sensor is attached is stationary.

6. The method of claim 4, further comprising:

combining said first bias estimate and said second bias estimate to generate an output bias estimate.

7. The method of claim 4, further comprising:

selecting one of said first bias estimate and said second bias estimate to generate an output bias estimate.

8. The method of claim 4, wherein said first bias estimating technique generates said first bias estimate based upon determining whether a device within which said sensor is located is stationary.

9. The method of claim 8, wherein said first bias estimating technique determines that said device is stationary when peak-to-peak values received from said sensor are less than a predetermined threshold.

10. The method of claim 4, wherein said first bias estimating technique generates said first bias estimate based upon detection of a pitch of a device within which said sensor is located.

11. The method of claim 4, wherein said first bias estimating technique generates said first bias estimate based upon detection of a change in angle of said device relative to one of a plurality of different axes.

12. The method of claim 10, wherein said first bias estimating technique calculates a difference between a first change of elevation angle determined using data output from said sensor and a second change of elevation angle determined using data output from another sensor.

13. The method of claim 12, wherein said sensor is an accelerometer and said another sensor is a sensor which measures rotation of said device about an axis.

14. The method of claim 10, wherein said first bias estimate is determined over a variable time window, said variable time window varying based upon measurement noise associated with data generated by said sensor.

15. The method of claim 10, wherein said first bias estimating technique operates in one mode to generate said first bias estimates as individual data points which converge over time.

16. The method of claim 10, wherein said first bias estimating technique operates in one mode to generate said first bias estimates using a line intersection technique associated with two individual bias estimates by combining two measurements of biases of pitch angular rates at two different roll angles.

17. The method of claim 10, wherein said first bias estimating technique operates in one mode to generate said first bias estimates by mapping averaged values output by said sensor into a bias of a yaw angular rate and a bias of a pitch angular rate defined in a user frame of reference.

18. The method of claim 10, wherein said first bias estimating technique operates in one of three modes to generate said first bias estimates including:

(a) a first mode for generating said first bias estimates as individual data points which converge over time;

(b) a second mode to generate said first bias estimates using a line intersection technique associated with two individual bias estimates; and

(c) a third mode for generating said first bias estimates by mapping averaged values output by said sensor into a bias of a yaw angular rate and a bias of a pitch angular rate defined in a user frame of reference.

19. The method of claim 18, wherein said first bias estimating technique operates in said third mode if (a) a measurement error variance associated with said first bias estimate is smaller than a first predetermined threshold, (b) a time window size over which said first bias estimate is calculated is smaller than a second predetermined threshold, and (c) a difference between a roll angle measured in a current time window and a roll angle measured in a previous time window is smaller than a third predetermined threshold.

20. The method of claim 18, wherein said first bias estimating technique operates in said second mode if (a) a difference between a roll angle measured in a current time window and a roll angle measured in a previous time window is greater than a first predetermined threshold and (b) a time gap between a current time window and a previous time window is shorter than a predetermined amount of time.

21. The method of claim 18, wherein said first bias estimating technique will change from operating in said third mode to subsequently operate in said first mode if a difference between a newest first bias estimate and a previous first bias estimate is greater than a predetermined value.

22. The method of claim 18, wherein said predetermined value is k*sqrt(P), where k is a predetermined constant, sqrt is a square root operation and P is a bias estimate error covariance value.

23. The method of claim 18, wherein said first bias estimating technique will change from operating in said third mode to subsequently operate in said first mode if a slope estimate from a bias estimating technique based on temperature is available.

24. The method of claim 4, wherein said first bias estimating technique generates said first bias estimate based upon slew-rate filtering.

25. The method of claim 24, wherein said slew rate filter calculates said first bias estimate as the sum of a previous first bias estimate plus y, where y is:

y={B;

x>

B,x;

−

B<

x<

B,−

B;

x<

−

B;

}

where x is an input from the sensor and B is a slew-rate filter threshold.

26. The method of claim 25, wherein B is periodically calculated based on at least one of a temperature change rate and a bias temperature slope estimate.

27. The method of claim 4, wherein said first bias estimating technique generates said first bias estimate based upon a detected temperature associated with a device within which said sensor is located.

28. The method of claim 27, wherein said first bias estimating technique generates said first bias estimate B by calculating:

B=slope(t)*T+intercept(t),

where T (in units of degrees Centigrade) is a temperature measurement associated with said device, slope(t) is a change of bias per unit temperature as a function of time, intercept(t) is a bias at a temperature of zero as a function of time and t is the time.

29. The method of claim 6, wherein said processing is performed by a Kalman filter to generate said output bias estimate.

30. The method of claim 29, wherein a process model associated with said Kalman filter can be expressed as:

31. The method of claim 4, wherein said sensor is one of a gyroscope, and a Coriolis effect rotational sensor.

32. The method of claim 4, wherein said sensor is a MEMS device.

33. A computer-readable medium containing instructions which, when executed on a processor, perform the step of:

estimating a bias associated with an sensor using at least a first bias estimating technique to generate a first bias estimate and a second bias estimating technique to generate a second bias estimate.

34. The computer-readable medium of claim 33, wherein said first bias estimate and said second bias estimate are associated with a non-zero value output from said sensor when an object to which said sensor is attached is stationary.

35. The computer-readable medium of claim 33, further comprising:

combining said first bias estimate and said second bias estimate to generate an output bias estimate.

36. The computer-readable medium of claim 33, further comprising:

selecting one of said first bias estimate and said second bias estimate to generate an output bias estimate.

37. The computer-readable medium of claim 33, wherein said first bias estimating technique generates said first bias estimate based upon determining whether a device within which said sensor is located is stationary.

38. The computer-readable medium of claim 37, wherein said first bias estimating technique determines that said device is stationary when peak-to-peak values received from said sensor are less than a predetermined threshold.

39. The computer-readable medium of claim 33, wherein said first bias estimating technique generates said first bias estimate based upon detection of a pitch of a device within which said sensor is located.

40. The computer-readable medium of claim 33, wherein said first bias estimating technique generates said first bias estimate based upon detection of a change in angle of said device relative to one of a plurality of different axes.

41. The computer-readable medium of claim 39, wherein said first bias estimating technique calculates a difference between a first change of elevation angle determined using data output from said sensor and a second change of elevation angle determined using data output from another sensor.

42. The computer-readable medium of claim 41, wherein said sensor is an accelerometer and said another sensor is a sensor which measures rotation of said device about an axis.

43. The computer-readable medium of claim 39, wherein said first bias estimate is determined over a variable time window, said variable time window varying based upon measurement noise associated with data generated by said sensor.

44. The computer-readable medium of claim 39, wherein said first bias estimating technique operates in one mode to generate said first bias estimates as individual data points which converge over time.

45. The computer-readable medium of claim 39, wherein said first bias estimating technique operates in one mode to generate said first bias estimates using a line intersection technique associated with two individual bias estimates by combining two adjacent measurements of biases of pitch angular rates at two different roll angles.

46. The computer-readable medium of claim 39, wherein said first bias estimating technique operates in one mode to generate said first bias estimates by mapping averaged values output by said sensor into a bias of a yaw angular rate and a bias of a pitch angular rate defined in a user frame of reference.

47. The computer-readable medium of claim 39, wherein said first bias estimating technique operates in one of three modes to generate said first bias estimates including:

(a) a first mode for generating said first bias estimates as individual data points which converge over time;

(b) a second mode to generate said first bias estimates using a line intersection technique associated with two individual bias estimates; and

(c) a third mode for generating said first bias estimates by mapping averaged values output by said sensor into a bias of a yaw angular rate and a bias of a pitch angular rate defined in a user frame of reference.

48. The computer-readable medium of claim 47, wherein said first bias estimating technique operates in said third mode if (a) a measurement error variance associated with said first bias estimate is smaller than a first predetermined threshold, (b) a time window size over which said first bias estimate is calculated is smaller than a second predetermined threshold, and (c) a difference between a roll angle measured in a current time window and a roll angle measured in a previous time window is smaller than a third predetermined threshold.

49. The computer-readable medium of claim 47, wherein said first bias estimating technique operates in said second mode if (a) a difference a difference between a roll angle measured in a current time window and a roll angle measured in a previous time window is greater than a first predetermined threshold and (b) a time gap between a current time window and a previous time window is shorter than a predetermined amount of time.

50. The computer-readable medium of claim 47, wherein said first bias estimating technique will change from operating in said third mode to subsequently operate in said first mode if a difference between a newest first bias estimate and a previous first bias estimate is greater than a predetermined value.

51. The computer-readable medium of claim 47, wherein said predetermined value is k*sqrt(P), where k is a predetermined constant, sqrt is a square root operation and P is a bias estimate error covariance value.

52. The computer-readable medium of claim 47, wherein said first bias estimating technique will change from operating in said third mode to subsequently operate in said first mode if a slope estimate from a bias estimating technique based on temperature is available.

53. The computer-readable medium of claim 33, wherein said first bias estimating technique generates said first bias estimate based upon slew-rate filtering.

54. The computer-readable medium of claim 53, wherein said slew rate filter calculates said first bias estimate as the sum of a previous first bias estimate plus y, where y is:

y={B;

x>

B,x;

−

B<

x<

B,−

B;

x<

−

B;

}

where x is an input from the sensor and B is a slew-rate filter threshold.

55. The computer-readable medium of claim 54, wherein B is periodically calculated based on at least one of a temperature change rate and a bias temperature slope estimate.

56. The computer-readable medium of claim 33, wherein said first bias estimating technique generates said first bias estimate based upon a detected temperature associated with a device within which said sensor is located.

57. The computer-readable medium of claim 56, wherein said first bias estimating technique generates said first bias estimate B by calculating:

B=slope(t)*T+intercept(t),

where T (in units of degrees Centigrade) is a temperature measurement associated with said device, slope(t) is a change of bias per unit temperature as a function of time, intercept(t) is a bias at a temperature of zero as a function of time and t is the time.

58. The computer-readable medium of claim 37, wherein said processing is performed by a Kalman filter to generate said output bias estimate.

59. The computer-readable medium of claim 58, wherein a process model associated with said Kalman filter can be expressed as:

60. The computer-readable medium of claim 33, wherein said sensor is one of a gyroscope, and a Coriolis effect rotational sensor.

61. The computer-readable medium of claim 33, wherein said sensor is a MEMS device.

62. A 3D pointing device comprising:

at least one sensor for detecting rotation of said 3D pointing device about at least one axis and generating a first output associated therewith;

an accelerometer for detecting acceleration of said 3D pointing device and generating a second output associated therewith; and

a processor for receiving said first and second outputs, determining a bias value associated with said first output using at least a first bias estimation technique and a second bias estimation technique and compensating said first output using said bias value.

63. A system comprising:

a device including at least one sensor generating a first output and an accelerometer generating a second output; and

a processor for receiving said first and second outputs, determining a bias value associated with said first output using at least a first bias estimation technique and a second bias estimation technique and compensating said first output using said bias value.

64. The system of claim 63, wherein said processor is disposed within said device.

65. The system of claim 63, wherein said processor is disposed in a second device that is in communication with said device.