SYSTEM AND METHOD FOR GENERATING A LOCATION ESTIMATE USING A METHOD OF INTERSECTIONS

• US 20080214205A1
• Filed: 01/31/2008
• Published: 09/04/2008
• Est. Priority Date: 02/05/2007
• Status: Active Grant

• Current Assignee: Geoscope Technologies Pte. Ltd. (Transpacific IP Group Limited)
• Original Assignee: Commscope Inc of North Carolina (CommScope Holding Company, Inc.)
• Inventors: Alles, Martin, Mazlum, Selcuk, Carlson, John, Maher, George
• Granted Patent: US 8,090,384 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G01S 5/021 Calibration, monitoring or correction G01S5/0252 takes precedence
• CPC Class Codes: G01S 5/0244 Accuracy or reliability of position solution or of measurements contributing thereto
• CPC Class Codes: G01S 5/02526 using non-dedicated equipment, e.g. user equipment or crowd-sourcing
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services

Abstract

The location of a wireless mobile device may be estimated using, at least in part, one or more pre-existing Network Measurement Reports (“NMRs”) which include calibration data for a number of locations within a geographic region. The calibration data for these locations is gathered and analyzed so that particular grid points within the geographic region can be determined and associated with a particular set or sets of calibration data from, for example, one or more NMRs. Regions may be defined as a function of any number of parameters and respective predetermined ranges thereof in the NMRs. An intersection of these defined regions may be determined and the location of a mobile device may be estimated as a function of the intersection.

Claims

1. A method of estimating the location of a mobile device in a geographic region comprising the steps of:

(a) providing calibration data for each of one or more calibration points in a geographic region, said calibration data including at least one characterizing parameter

(b) receiving a network measurement report from a mobile device at an unknown location, said network measurement report also including at least one characterizing parameter;

(c) defining a first region within said calibration data as a function of a first characterizing parameter of said network measurement report and a predetermined range of said first parameter;

(d) defining a second region within said calibration data as a function of another characterizing parameter of said network measurement report and a predetermined range of said another parameter;

(e) repeating steps (c)-(d) for each characterizing parameter in said network measurement report;

(f) determining an intersection of each defined region; and

(g) estimating the location of a mobile device in said geographic region as a function of said intersection.

2. The method of claim 1 wherein one of said calibration points is located on a predetermined fixed uniform grid defined over said geographic region.

3. The method of claim 1 wherein one of said calibration points is randomly located within said geographic region.

4. The method of claim 1 wherein said calibration data comprises information from at least one network measurement report having a known location.

5. The method of claim 1 wherein said calibration data for one of said calibration points is obtained from one or more mobile devices located in close proximity to said one calibration point.

6. The method of claim 1 wherein said calibration data for one of said calibration points is obtained from a signal transmitted from a mobile device in close proximity to said one calibration point and received at a receiver in or in proximity to said geographic region.

7. The method of claim 1 wherein for each of select ones of said calibration points the calibration data includes plural data vectors and the evaluating of said calibration data comprises a determination of clustering of said plural data vectors.

8. The method of claim 1 wherein the step of estimating the location of a mobile device further comprises estimating the location of a mobile device as the centroid of said intersection.

9. The method of claim 1 wherein the step of estimating the location of a mobile device further comprises estimating the location of a mobile device as a function of a statistical measure for calibration data of select ones of said calibration points.

10. The method of claim 9 wherein said statistical measure is a probability density function.

11. The method of claim 1 wherein the step of determining an intersection further comprises determining an intersection of each defined region as a function of a distortion measure.

12. The method of claim 11 wherein the distortion measure is selected from the group consisting of:

the largest width of any network measurement report parameter level, a weighted combination of individual widths of any network measurement report parameter level, the area of the intersection, or any combination thereof.

13. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) comparing an ordered list of cells neighboring a cell serving said mobile device in said network measurement report to an ordered list of neighboring cells in each calibration point of said intersection, said ordering being a function of any parameter of said network measurement report; and

(ii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a centroid of a cluster of matching calibration points in said intersection;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said network measurement report.

14. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) comparing an ordered list of cells neighboring a cell serving said mobile device in said network measurement report to an ordered list of neighboring cells in each calibration point of said intersection, said ordering being a function of any parameter of said network measurement report;

(ii) if no exact match is made between the ordered list of neighboring cells of said network measurement report and any calibration point in said intersection then forming a largest subset of the ordered list of neighboring cells in said calibration points that provide a match; and

(iii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a centroid of a cluster of matching calibration points in said intersection;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said network measurement report.

15. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) comparing an ordered list of cells neighboring a cell serving said mobile device in said network measurement report to an ordered list of neighboring cells in each calibration point of said intersection, said ordering being a function of any parameter of said network measurement report;

(ii) if the ordered list of neighboring cells of said network measurement report is not contained in the ordered list of neighboring cells for said intersection then using a largest subset of ordered neighboring cells in said network measurement report having either an exact match or contained in the ordered list of neighboring cells in said intersection; and

(iii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a centroid of a cluster of matching calibration points in said intersection;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said network measurement report.

16. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) evaluating a probability density function for each power level of a cell neighboring a cell serving said mobile device in said network measurement report over each calibration point in a set of available calibration points in said intersection;

(ii) determining a joint probability as a function of said individual probability density functions; and

(iii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said network measurement report.

17. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) evaluating a joint probability of power levels for at least one cell neighboring a cell serving said mobile device in said network measurement report over a set of available calibration points in said intersection; and

(ii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said network measurement report.

18. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) determining a distortion measure between a parameter or function of a calibration point and a corresponding parameter or function in said network measurement report; and

(ii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a location of a calibration point having the smallest distortion measure;

a weighting of the locations of a set of matching calibration points in said intersection, said weighting being a function of said distortion measure;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said network measurement report.

19. The method of claim 18 wherein said distortion measure is a Mahalanobis distance.

20. The method of claim 1 wherein the step of estimating the location of a mobile device comprises:

(i) matching cell power ordering of cells neighboring a cell serving said mobile device in said network measurement report to neighboring cell power ordering of calibration points in each of the calibration points in said intersection; and

(ii) selecting an estimated location as a function of a quality of said matching,

wherein said quality is a function of a relative shift in an ordering sequence occurring between said network measurement report and calibration point cell power ordering.

21. The method of claim 1 further comprising the step of:

(h) examining subsets of available network measurement reports for an intersection.

22. A method of estimating the location of a mobile device in a geographic region comprising the steps of:

(a) providing calibration data for a plurality of calibration points in a geographic region, said calibration data having at least one characterizing parameter;

(b) receiving a set of network measurement reports from a mobile device at an unknown location, at least one network measurement report in said set also including at least one characterizing parameter and for each of select ones of said network measurement reports the report includes plural data vectors;

(c) defining a first region within said calibration data as a function of a first characterizing parameter of said network measurement report and a predetermined range of said first parameter;

(d) defining a second region within said calibration data as a function of another characterizing parameter of said network measurement report and a predetermined range of said another parameter;

(e) repeating steps (c)-(d) for each characterizing parameter in said set of network measurement reports;

(f) determining a clustering of said plural data vectors; and

(g) estimating the location of a mobile device in said geographic region as a function of said clustering.

23. The method of claim 22 wherein one of said calibration points is located on a predetermined fixed uniform grid defined over said geographic region.

24. The method of claim 22 wherein one of said calibration points is randomly located within said geographic region.

25. The method of claim 22 wherein said calibration data comprises information from at least one network measurement report.

26. The method of claim 22 wherein said calibration data for one of said calibration points is obtained from one or more mobile devices located in close proximity to said one calibration point.

27. The method of claim 22 wherein said calibration data for one of said calibration points is obtained from a signal transmitted from a mobile device in close proximity to said one calibration point and received at a receiver in or in proximity to said geographic region.

28. The method of claim 22 wherein said clustering is determined as a function of a metric.

29. The method of claim 28 wherein said metric is selected from the group consisting of:

joint probability, Mahalanobis distance, and cluster radius.

30. A method of estimating the location of a mobile device in a geographic region comprising the steps of:

(a) providing calibration data for each of one or more calibration points in a geographic region, said calibration data including at least one characterizing parameter

(b) receiving a set of network measurement reports from a mobile device at an unknown location, at least one of said network measurement reports in said set also including at least one characterizing parameter;

(c) determining a representative value for each available characterizing parameter in said set as a function of a variation of said available characterizing parameter in each network measurement report in said set;

(d) determining one or more representative network measurement reports as a function of said representative value; and

(e) estimating the location of a mobile device in said geographic region as a function of said one or more representative network measurement reports.

31. The method of claim 30 wherein the step of determining a representative value further comprises obtaining a representative value as a function of an available set of representative values.

32. The method of claim 31 wherein said representative value is determined as a function of a mean or median of said available set of representative values.

33. The method of claim 30 wherein the step of determining a representative value further comprises selecting a representative value as a function of a predetermined range.

34. The method of claim 30 wherein one of said calibration points is located on a predetermined fixed uniform grid defined over said geographic region.

35. The method of claim 30 wherein one of said calibration points is randomly located within said geographic region.

36. The method of claim 30 wherein said calibration data comprises information from at least one network measurement report.

37. The method of claim 30 wherein said calibration data for one of said calibration points is obtained from one or more mobile devices located in close proximity to said one calibration point.

38. The method of claim 30 wherein said calibration data for one of said calibration points is obtained from a signal transmitted from a mobile device in close proximity to said one calibration point and received at a receiver in or in proximity to said geographic region.

39. The method of claim 30 wherein for each of select ones of said calibration points the calibration data includes plural data vectors and the evaluating of said calibration data comprises a determination of clustering of said plural data vectors.

40. The method of claim 30 further comprising the steps of:

(e) defining a first region as a function of a first representative value for a first characterizing parameter and a predetermined range of said first value;

(f) defining a second region as a function of a second representative value for a second characterizing parameter and a predetermined range of said second value;

(g) repeating steps (e)-(f) for each characterizing parameter in said one or more representative network measurement reports;

(h) determining an intersection of each defined region; and

(i) estimating the location of a mobile device in said geographic region as a function of said intersection.

41. The method of claim 40 wherein the step of estimating the location of a mobile device further comprises estimating the location of a mobile device as the centroid of said intersection.

42. The method of claim 40 wherein the step of estimating the location of a mobile device further comprises estimating the location of a mobile device as a function of a statistical measure for calibration data of select ones of said calibration points.

43. The method of claim 42 wherein said statistical measure is a probability density function.

44. The method of claim 40 wherein the step of determining an intersection further comprises determining an intersection of each defined region as a function of a distortion measure.

45. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) comparing an ordered list of cells neighboring a cell serving said mobile device in said one or more representative network measurement reports to an ordered list of neighboring cells in each calibration point of said intersection, said ordering being a function of any parameter of the one or more representative network measurement reports; and

(ii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a centroid of a cluster of matching calibration points in said intersection;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said one or more representative network measurement reports.

46. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) comparing an ordered list of cells neighboring a cell serving said mobile device in said one or more representative network measurement reports to an ordered list of neighboring cells in each calibration point of said intersection, said ordering being a function of any parameter in said one or more representative network measurement reports;

(ii) if no exact match is made between the ordered list of neighboring cells of said one or more representative network measurement reports and any calibration point in said intersection then forming a largest subset of the ordered list of neighboring cells in said calibration points that provide a match; and

(iii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a centroid of a cluster of matching calibration points in said intersection;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said one or more representative network measurement reports.

47. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) comparing an ordered list of cells neighboring a cell serving said mobile device in said one or more representative network measurement reports to an ordered list of neighboring cells in each calibration point of said intersection, said ordering being a function of any parameter in said one or more representative network measurement reports;

(ii) if the ordered list of neighboring cells of said one or more representative network measurement reports is not contained in the ordered list of neighboring cells for said intersection then using a largest subset of ordered neighboring cells in said one or more representative network measurement reports having either an exact match or contained in the ordered list of neighboring cells in said intersection; and

(iii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a centroid of a cluster of matching calibration points in said intersection;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said one or more representative network measurement reports.

48. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) evaluating a probability density function for each power level of a cell neighboring a cell serving said mobile device in said one or more representative network measurement reports over each calibration point in a set of available calibration points in said intersection;

(ii) determining a joint probability as a function of said individual probability density functions; and

(iii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said one or more representative network measurement reports.

49. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) evaluating a joint probability of power levels for at least one cell neighboring a cell serving said mobile device in said one or more representative network measurement reports over a set of available calibration points in said intersection; and

(ii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a highest joint probability matching calibration point in said intersection;

a weighted sum of the locations of a set of matching calibration points in said intersection;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said one or more representative network measurement reports.

50. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) determining a distortion measure between a parameter or function of a calibration point and a corresponding parameter or function in said one or more network measurement reports; and

(ii) generating an estimated location of said mobile device wherein said estimated location is selected from the group consisting of;

a location of a calibration point having the smallest distortion measure;

a weighting of the locations of a set of matching calibration points in said intersection, said weighting being a function of said distortion measure;

a weighted sum of clustered locations of a set of matching calibration points in said intersection; and

as a function of estimated locations determined from a subset of said one or more representative network measurement reports.

51. The method of claim 50 wherein said distortion measure is a Mahalanobis distance.

52. The method of claim 40 wherein the step of estimating the location of a mobile device comprises:

(i) matching cell power ordering of cells neighboring a cell serving said mobile device in said one or more representative network measurement reports to neighboring cell power ordering of calibration points in each of the calibration points in said intersection; and

(ii) selecting an estimated location as a function of a quality of said matching,

wherein said quality is a function of a relative shift in an ordering sequence occurring between said one or more representative network measurement reports and calibration point cell power ordering.

53. A system for estimating the location of a mobile device in a geographic region comprising:

(a) a database; and

(b) a processor for receiving calibration data for each of one or more calibration points in a geographic region and for receiving at least one network measurement report from a mobile device at an unknown location, said calibration data and network measurement report having at least one characterizing parameter and said processor is programmed to;

(i) determine a first region within said calibration data as a function of a first characterizing parameter of said at least one network measurement report and a predetermined range of said first parameter and repeat said determination for each characterizing parameter in said network measurement report,

(ii) determine an intersection for each defined region, and

(iii) estimate the location of a mobile device in said geographic region as a function of said intersection.

54. The system of claim 53 wherein one of said calibration points is located on a predetermined fixed uniform grid defined over said geographic region.

55. The system of claim 53 wherein one of said calibration points is randomly located within said geographic region.

56. The system of claim 53 wherein said calibration data comprises information from at least one network measurement report.

57. The system of claim 53 wherein said calibration data for one of said calibration points is obtained from one or more mobile devices located in close proximity to said one calibration point.

58. The system of claim 53 wherein said calibration data for one of said calibration points is obtained from a signal transmitted from a mobile device in close proximity to said one calibration point and received at a receiver in or in proximity to said geographic region.

59. The system of claim 53 wherein for each of select ones of said calibration points the calibration data includes plural data vectors and the processor is further programmed to evaluate said calibration data to determine a clustering of said plural data vectors.

60. A system for estimating the location of a mobile device in a geographic region comprising:

(a) a database; and

(b) a processor for receiving calibration data for each of a plurality of calibration points in a geographic region, said calibration data having at least one characterizing parameter and for receiving a set of network measurement reports from a mobile device at an unknown location, at least one of said network measurement reports in said set also including at least one characterizing parameter, said processor being programmed to;

(i) determine a representative value for each available characterizing parameter in said set as a function of a variation of said available characterizing parameter in each network measurement report in said set;

(ii) determine one or more representative network measurement reports as a function of said representative value; and

(iii) estimate the location of a mobile device in said geographic region as a function of said one or more representative network measurement reports.

61. The system of claim 60 wherein the processor is further programmed to:

(iv) determine a first region as a function of a first representative value for a first characterizing parameter and a predetermined range of said first value and repeat said determination for each characterizing parameter in said one or more representative network measurement reports;

(v) determining an intersection of each determined region; and

(vi) estimating the location of a mobile device in said geographic region as a function of said intersection.

62. The system of claim 60 wherein one of said calibration points is located on a predetermined fixed uniform grid defined over said geographic region.

63. The system of claim 60 wherein one of said calibration points is randomly located within said geographic region.

64. The system of claim 60 wherein said calibration data comprises information from at least one network measurement report.

65. The system of claim 60 wherein said calibration data for one of said calibration points is obtained from one or more mobile devices located in close proximity to said one calibration point.

66. The system of claim 60 wherein said calibration data for one of said calibration points is obtained form a signal transmitted from a mobile device in close proximity to said one calibration point and received at a receiver in or in proximity to said geographic region.

67. The system of claim 60 wherein for each of select ones of said calibration points the calibration data includes plural data vectors and the processor is further programmed to evaluate said calibration data to determine a clustering of said plural data vectors.