Context-based parameter maps for position determination

• US 9,736,638 B2
• Filed: 04/10/2015
• Issued: 08/15/2017
• Est. Priority Date: 12/31/2012
• Status: Expired due to Fees

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Khorashadi, Behrooz, Poduri, Sameera, Pakzad, Payam, Sridhara, Vinay
• Primary Examiner(s): Appiah, Charles
• Assistant Examiner(s): Mastrodonato, Margaret G
• Application Number:
  US14/684,112
• Publication Number:
  US 20150215740A1
• Time in Patent Office: 858 Days
• Field of Search: None
• 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;

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

determining a representation parameter for said context parameter map based, at least in part, on said measurement value and said estimated location;

determining a discriminative ability of said context parameter map with respect to said representation parameter;

determining an expected gain associated with said context parameter map; and

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

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 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.

11. The method of claim 10, wherein said state of said user comprises sitting, standing, or moving.

12. 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.

13. 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.

14. 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.

15. 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;

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;

means for determining a representation parameter for said context parameter map based, at least in part, on said measurement value and said estimated location;

means for determining a discriminative ability of said context parameter map with respect to said representation parameter;

means for determining an expected gain associated with said context parameter map; and

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

16. The apparatus of claim 15, 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.

17. 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;

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

determine a representation parameter for said context parameter map based, at least in part, on said measurement value and said estimated location;

determine a discriminative ability of said context parameter map with respect to said representation parameter;

determine an expected gain associated with said context parameter map; and

selectively download said context parameter map from a server based, at least in part, on a sensing capability of said mobile station, said expected gain, and said discriminative ability.

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, wherein at least one of said user attributes comprises age, occupation, shopping preferences, or club membership.

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 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.

26. The apparatus of claim 25, wherein said state of said user comprises sitting, standing, or moving.

27. The apparatus of claim 17, 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.

28. The apparatus of claim 17, 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.

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

30. 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 said signal from said sensor;

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

determine a representation parameter for said context parameter map based, at least in part, on said measurement value and said estimated location;

determine a discriminative ability of said context parameter map with respect to said representation parameter;

determine an expected gain associated with said context parameter map; and

selectively download said context parameter map from a server based, at least in part, on a sensing capability of said mobile station, said expected gain, and said discriminative ability.