System and method for improving orientation data

• US 9,495,018 B2
• Filed: 10/12/2012
• Issued: 11/15/2016
• Est. Priority Date: 11/01/2011
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Kulik, Victor
• Primary Examiner(s): Breene, John
• Assistant Examiner(s): Islam, Mohammad
• Application Number:
  US13/651,350
• Publication Number:
  US 20130110450A1
• Time in Patent Office: 1,495 Days
• US Class Current: 1/1
• CPC Class Codes: G01B 21/22 for measuring angles or tapers; for testing the alignment of axes
• CPC Class Codes: G01C 17/38 Testing, calibrating, or compensating of compasses
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G06F 15/00 Digital computers in general details G06F1/00 – G06F13/00; Data processing equipment in general
• CPC Class Codes: G06F 2200/1637 Sensing arrangement for detection of housing movement or orientation, e.g. for controlling scrolling or cursor movement on the display of an handheld computer
• CPC Class Codes: G06F 3/0304 Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• 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

Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for improving orientation data. In one embodiment, techniques are described for filtering data associated with a first sensor coupled to a computing device, by receiving a signal from the first sensor, detecting a change in a variability of a first signal parameter from a plurality of signal parameters from the signal, and adjusting, based at least in part on the detected change in the variability of the first signal parameter, at least one filter parameter of a filter used to filter a second signal parameter from the signal.

Claims

1. A method for filtering data associated with a first sensor coupled to a computing device, the method comprising:

receiving a signal from the first sensor;

detecting a change in a variability of a first signal parameter from a plurality of signal parameters from the signal;

adjusting, based at least in part on the detected change in the variability of the first signal parameter, a first filter parameter of a filter used to filter a second signal parameter from the signal, wherein the first filter parameter of the filter comprises a window size for the filter, and wherein adjusting the first filter parameter of the filter comprises changing the window size for the filter directly proportionally to the variability of the first signal parameter; and

transitioning the filter to a low power state in response to determining that the device has transitioned to a stationary state.

2. The method of claim 1, wherein the filter is an adaptive filter and the first filter parameter of the adaptive filter is dynamically adjustable.

3. The method of claim 2, wherein the adaptive filter is a Kalman filter.

4. The method of claim 1, wherein a second filter parameter for the filter comprises a gain for the filter, and wherein adjusting the second filter parameter of the filter comprises changing the gain for the filter inversely proportionally to the variability of the first signal parameter.

5. The method of claim 1, wherein the first sensor is an accelerometer, the second signal parameter is a gravity vector and the first signal parameter is a signal parameter other than the gravity vector.

6. The method of claim 5, wherein the first signal parameter is one or more of a signal noise, a measurement error, a sensor calibration error or a linear acceleration of the device.

7. The method of claim 1, wherein the first sensor is a magnetometer, the second signal parameter is a magnetic vector and the first signal parameter is a signal parameter other than the magnetic vector.

8. The method of claim 7, wherein the first signal parameter is one or more of a signal noise, a sensor calibration error or a magnetic transient field.

9. The method of claim 1, wherein detecting the change in the variability of the first signal parameter comprises determining that the device has transitioned to the stationary state.

10. The method of claim 1, further comprising transitioning the first sensor to a low power state in response to determining, using a second sensor, that the device has transitioned to the stationary state.

11. The method of claim 10, wherein the first sensor is a gyroscope or/and a magnetometer and the second sensor is an accelerometer.

12. The method of claim 10, wherein transitioning to the low power state comprises switching off or duty cycling the first sensor.

13. A computer device for filtering data associated with a first sensor, comprising:

a receiver configured to receive a signal from the first sensor;

a filter having a first filter parameter used to filter a first signal parameter from the signal, the first filter parameter being adjustable based at least in part on a detected change in variability of a second signal parameter from a plurality of signal parameters from the signal, wherein the first filter parameter of the filter comprises a window size for the filter, wherein the window size for the filter is directly proportional to the variability of the second signal parameter; and

processing logic configured to transition the filter to a low power state in response to a determination that the device has transitioned to a stationary state.

14. The computer device of claim 13, wherein the filter is an adaptive filter and the first filter parameter of the adaptive filter is dynamically adjustable.

15. The computer device of claim 14, wherein the adaptive filter is a Kalman filter.

16. The computer device of claim 13, wherein a second filter parameter comprises a gain for the filter, wherein the gain for the filter is inversely proportional to the variability of the second signal parameter.

17. The computer device of claim 13, wherein the first sensor is an accelerometer, the first signal parameter is a gravity vector and the second signal parameter is a signal parameter other than the gravity vector.

18. The computer device of claim 17, wherein the second signal parameter is one or more of a signal noise, a measurement error, a sensor calibration error or a linear acceleration of the device.

19. The computer device of claim 13, wherein the first sensor is a magnetometer, the first signal parameter is a magnetic vector and the second signal parameter is a signal parameter other than the magnetic vector.

20. The computer device of claim 19, wherein the second signal parameter is one or more of a signal noise, a sensor calibration error or a magnetic transient field.

21. The computer device of claim 13, wherein the change in the variability of the second signal parameter is detected based on determining that the device has transitioned to the stationary state.

22. The computer device of claim 13, further comprising the processing logic configured to transition the first sensor to a low power state in response to a determination, made using a second sensor, that the device has transitioned to the stationary state.

23. The computer device of claim 22, wherein the first sensor is a gyroscope or/and a magnetometer and the second sensor is an accelerometer.

24. The computer device of claim 22, wherein the transition to the low power state comprises switching off or duty cycling the first sensor.

25. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium comprises instructions executable by processing logic, the instructions comprising instructions to:

receive a signal from a first sensor;

detect a change in a variability of a first signal parameter from a plurality of signal parameters from the signal;

adjust, based at least in part on the detected change in the variability of the first signal parameter, at least one filter parameter of a filter used to filter a second signal parameter from the signal, wherein the at least one filter parameter of the filter comprises a window size for the filter, wherein the window size for the filter is directly proportional to the variability of the second signal parameter; and

transition the filter to a low power state in response to determining that the apparatus has transitioned to a stationary state.

26. An apparatus, comprising:

means for receiving a signal from a first sensor;

means for detecting a change in a variability of a first signal parameter from a plurality of signal parameters from the signal;

means for adjusting, based at least in part on the detected change in the variability of the first signal parameter, a first filter parameter of a filter used to filter a second signal parameter from the signal, wherein the first filter parameter of the filter comprises a window size for the filter, and wherein adjusting the first filter parameter of the filter comprises means for changing the window size for the filter directly proportionally to the variability of the first signal parameter; and

means for transitioning the filter to a low power state in response to determining that the apparatus has transitioned to a stationary state.

27. The apparatus of claim 26, wherein the filter is an adaptive filter and the first filter parameter of the adaptive filter is dynamically adjustable.

28. The apparatus of claim 26, wherein a second filter parameter for the filter comprises a gain for the filter, wherein the gain for the filter is inversely proportional to the variability of the first signal parameter.