CONTEXT-BASED PARAMETER MAPS FOR POSITION DETERMINATION

• US 20140187258A1
• Filed: 03/14/2013
• Published: 07/03/2014
• Est. Priority Date: 12/31/2012
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Khorashadi, Behrooz, Poduri, Sameera, Sridhara, Vinay, Pakzad, Payam
• Granted Patent: US 9,031,573 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G01C 21/005 with correlation of navigation data from several sources, e.g. map or contour matching G01C21/30 takes precedence
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

In one implementation, a method may comprise: storing a user profile indicative of at least one attribute of a user of a mobile station; determining a measurement value based, at least in part, on a signal from at least one sensor on the mobile station; and estimating a location of the mobile station based, at least in part, on an association of the at least one attribute and the measurement value with a context parameter map database.

Claims

1. A method comprising, at a mobile station:

determining a measurement value based, at least in part, on a signal from at least one sensor on said mobile station; and

estimating a location of said mobile station based, at least in part, on an association of said measurement value with a context parameter map.

2. The method of claim 1, wherein said context parameter map comprises a combined context parameter map including at least a first context parameter map associating user attributes with a first set of particular locations and a second context parameter map associating a first type of sensor measurements with a second set of particular locations.

3. The method of claim 2, wherein said combined context parameter map further comprises a third context parameter map associating a second type of sensor measurements with a third set of particular locations.

4. The method of claim 2, further comprising storing a user profile indicative of at least one attribute of a user of the mobile station.

5. The method of claim 4, wherein said at least one attribute of said user comprises age, occupation, shopping preferences, or club membership.

6. The method of claim 2, wherein said first context parameter map associates an age, an occupation, a club membership, or a shopping history of a user of said mobile station with said first set of particular locations.

7. The method of claim 2, wherein said second context parameter map associates light or sound intensity or spectra with said second set of particular locations.

8. The method of claim 2, wherein said second context parameter map associates temperature or pressure with said second set of particular locations.

9. The method of claim 1, wherein values in said context parameter map are time-dependent.

10. The method of claim 1, wherein said at least one sensor comprises an inertial sensor, a proximity sensor, a temperature sensor, a compass, a gravitometer, or an audio sensor.

11. The method of claim 1, wherein said estimating said location of said mobile station is further based, at least in part, on an association of a state of a user of said mobile station and said measurement value with said context parameter map.

12. The method of claim 11, wherein said state of said user comprises sitting, standing, or moving.

13. The method of claim 1, further comprising:

selectively downloading said context parameter map from a server based, at least in part, on a sensing capability of said mobile station.

14. The method of claim 1, further comprising:

selectively downloading said context parameter map from a server based, at least in part, on expected energy consumption and accuracy of a particular sensor of said mobile station, wherein measurements from said particular sensor correspond to one or more parameters included in said context parameter map.

15. The method of claim 1, further comprising:

classifying a motion of a user of the mobile station based, at least in part, on one or more signals received from said at least one sensor, wherein estimating the location is based, at least in part, on the classified motion.

16. The method of claim 1, wherein estimating a location of said mobile station is further based, at least in part, on an association of at least one attribute of a user of the mobile station and said measurement value with said context parameter map.

17. An apparatus comprising:

means for determining a measurement value based, at least in part, on a signal from at least one sensor on a mobile station; and

means for estimating a location of said mobile station based, at least in part, on an association of said measurement value with a context parameter map.

18. The apparatus of claim 17, wherein said context parameter map comprises a combined context parameter map including at least a first context parameter map associating user attributes with a first set of particular locations and a second context parameter map associating a first type of sensor measurements with a second set of particular locations.

19. The apparatus of claim 18, wherein said combined context parameter map further comprises a third context parameter map associating a second type of sensor measurements with a third set of particular locations.

20. The apparatus of claim 18, further comprising means for storing a user profile indicative of at least one attribute of a user of the mobile station.

21. The apparatus of claim 18, wherein said first context parameter map associates an age, an occupation, a club membership, or a shopping history of a user of said mobile station with said first set of particular locations.

22. The apparatus of claim 18, wherein said second context parameter map associates light or sound intensity or spectra with said second set of particular locations.

23. The apparatus of claim 18, wherein said second context parameter map associates temperature or pressure with said second set of particular locations.

24. The apparatus of claim 17, wherein values in said context parameter map are time-dependent.

25. The apparatus of claim 17, wherein said at least one sensor comprises an inertial sensor, a proximity sensor, a temperature sensor, a compass, a gravitometer, or an audio sensor.

26. The apparatus of claim 17, wherein said means for estimating said location of said mobile station comprises means for estimating said location based, at least in part, on an association of a state of a user of said mobile station and said measurement value with said context parameter map, said state comprising sitting, standing, or moving.

27. The apparatus of claim 17, further comprising:

means for selectively downloading said context parameter map from a server based, at least in part, on a sensing capability of said mobile station.

28. The apparatus of claim 17, further comprising:

means for selectively downloading said context parameter map from a server based, at least in part, on expected energy consumption and accuracy of a particular sensor of said mobile station, wherein measurements from said particular sensor correspond to one or more parameters included in said context parameter map.

29. The apparatus of claim 17, further comprising:

means for classifying a motion of a user of the mobile station based, at least in part, on one or more signals received from said at least one sensor, wherein estimating the location is based, at least in part, on the classified motion.

30. An apparatus comprising:

a sensor configured to produce a signal based, at least in part, on a measurement of a sensor parameter; and

one or more processing units configured to;

determine a measurement value based, at least in part, on said signal from said sensor; and

estimate a location of a mobile station based, at least in part, on an association of said measurement value with a context parameter map.

31. The apparatus of claim 30, wherein said context parameter map comprises a combined context parameter map including at least a first context parameter map associating user attributes with a first set of particular locations and a second context parameter map associating a first type of sensor measurements with a second set of particular locations.

32. The apparatus of claim 31, wherein said combined context parameter map further comprises a third context parameter map associating a second type of sensor measurements with a third set of particular locations.

33. The apparatus of claim 31, further comprising a memory configured to store a user profile indicative of at least one attribute of a user of the mobile station.

34. The apparatus of claim 36, wherein said at least one attribute of said user comprises age, occupation, shopping preferences, or club membership.

35. The apparatus of claim 31, wherein said first context parameter map associates an age, an occupation, a club membership, or a shopping history of a user of said mobile station with said first set of particular locations.

36. The apparatus of claim 31, wherein said second context parameter map associates light or sound intensity or spectra with said second set of particular locations.

37. The apparatus of claim 31, wherein said second context parameter map associates temperature or pressure with said second set of particular locations.

38. The apparatus of claim 30, wherein values in said context parameter map are time-dependent.

39. The apparatus of claim 30, wherein said sensor comprises an inertial sensor, a proximity sensor, a temperature sensor, a compass, a gravitometer, or an audio sensor.

40. The apparatus of claim 30, wherein said estimating said location of said mobile station is further based, at least in part, on an association of a state of a user of said mobile station and said measurement value with said context parameter map.

41. The apparatus of claim 40, wherein said state of said user comprises sitting, standing, or moving.

42. The apparatus of claim 30, wherein said one or more processing units are configured to selectively download said context parameter map from a server based, at least in part, on a sensing capability of said mobile station.

43. The apparatus of claim 30, wherein said one or more processing units are configured to selectively download said context parameter map from a server based, at least in part, on expected energy consumption and accuracy of a particular sensor of said mobile station, wherein measurements from said particular sensor correspond to one or more parameters included in said context parameter map.

44. The apparatus of claim 30, wherein said one or more processing units are configured to classify a motion of a user of the mobile station based, at least in part, on one or more signals received from said sensor, wherein estimating the location is based, at least in part, on the classified motion.

45. The apparatus of claim 30, wherein said one or more processing units are configured to estimate a location of said mobile station based, at least in part, on an association of at least one attribute of a user of the mobile station and said measurement value with said context parameter map.

46. A non-transitory storage medium comprising machine-readable instructions stored thereon that are executable by a special purpose computing device to:

determine a measurement value based, at least in part, on a signal from at least one sensor on a mobile station; and

estimate a location of said mobile station based, at least in part, on an association of said measurement value with a context parameter map.