SYSTEM AND METHOD FOR IMPROVING ORIENTATION DATA

• US 20170131094A1
• Filed: 07/25/2016
• Published: 05/11/2017
• Est. Priority Date: 11/01/2011
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Kulik, Victor
• Granted Patent: US 9,995,575 B2
• Time in Patent Office: Days
• 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 sensor coupled to a computing device, by receiving a signal from the 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; and

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

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

3. The method of claim 1, wherein the filter is a Kalman filter.

4. 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.

5. The method of claim 4, 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 computing device.

6. 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.

7. The method of claim 6, wherein the first signal parameter is one or more of a signal noise, a sensor calibration error or a magnetic transient field.

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

9. 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 computing device has transitioned to a stationary state.

10. The method of claim 9, wherein the first sensor is a gyroscope or a magnetometer and the second sensor is an accelerometer.

11. The method of claim 9, wherein transitioning to the low power state comprises switching off or duty cycling the first sensor.

12. A computing device for filtering data, comprising:

a sensor coupled to the computing device;

a receiver coupled to the sensor and configured to receive a signal from the sensor; and

a filter having a filter parameter used to filter a first signal parameter from the signal, the 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 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.

13. The computing device of claim 12, wherein the filter is an adaptive filter and the filter parameter of the adaptive filter is dynamically adjustable.

14. The computing device of claim 12, wherein the sensor is an accelerometer, the first signal parameter is a gravity vector and 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 computing device.

15. The computing device of claim 12, wherein the sensor is a magnetometer, the first signal parameter is a magnetic vector and the second signal parameter is one or more of a signal noise, a sensor calibration error or a magnetic transient field.

16. 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; and

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

17. The method of claim 16, wherein the filter is an adaptive filter and the filter parameter of the adaptive filter is dynamically adjustable.

18. The method of claim 16, wherein the filter is a Kalman filter.

19. The method of claim 16, 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.

20. The method of claim 19, 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 computing device.

21. The method of claim 16, 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.

22. The method of claim 21, wherein the first signal parameter is one or more of a signal noise, a sensor calibration error or a magnetic transient field.

23. The method of claim 16, wherein detecting the change in the variability of the first signal parameter comprises determining that the computing device has transitioned to a stationary state.

24. The method of claim 16, further comprising transitioning the first sensor to a low power state in response to determining, using a second sensor, that the computing device has transitioned to a stationary state.

25. The method of claim 24, wherein the first sensor is a gyroscope or a magnetometer and the second sensor is an accelerometer.

26. The method of claim 24, wherein transitioning to the low power state comprises switching off or duty cycling the first sensor.

27. A computing device for filtering data, comprising:

a sensor coupled to the computing device;

a receiver coupled to the computing device and configured to receive a signal from the sensor; and

a filter having a filter parameter used to filter a first signal parameter from the signal, the 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 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.

28. The computing device of claim 27, wherein the filter is an adaptive filter and the filter parameter of the adaptive filter is dynamically adjustable.

29. The computing device of claim 27, wherein the sensor is an accelerometer, the first signal parameter is a gravity vector and 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 computing device.

30. The computing device of claim 27, wherein the sensor is a magnetometer, the first signal parameter is a magnetic vector and the second signal parameter is one or more of a signal noise, a sensor calibration error or a magnetic transient field.