SYSTEM AND METHOD FOR OPTIMIZING LOCATION ESTIMATE OF MOBILE UNIT

• US 20080188242A1
• Filed: 01/14/2008
• Published: 08/07/2008
• Est. Priority Date: 02/05/2007
• Status: Active Grant

• Current Assignee: Geoscope Technologies Pte. Ltd. (Transpacific IP Group Limited)
• Original Assignee: Andrew Corporation (CommScope Holding Company, Inc.)
• Inventors: Alles, Martin, Mazlum, Selcuk, Carlson, John, Maher, George
• Granted Patent: US 8,311,018 B2
• Time in Patent Office: Days
• US Class Current: 455/456.5
• 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. Embodiments of the present subject matter also provide a method of improving a location estimate of a mobile device. Received signal level measurements reported by a mobile device for which a location estimate is to be determined may be evaluated and/or compared with the characteristics associated with the various grid points to estimate the location of the mobile device.

Claims

24. A method of improving a location estimate of a mobile device comprising the steps of:

(a) providing a plurality of grid points in a geographic region;

(b) providing a set of network measurement reports for a mobile device in the geographic region, said set of network measurement reports including one or more subsets of network measurement reports;

(c) comparing ones of said plurality of grid points to at least one parameter of a subset of said network measurement reports;

(d) generating a first location estimate of the mobile device for each subset of network measurement reports (“

NMR”

);

(e) determining a second location estimate of the mobile device as a function of at least one of said generated first location estimates; and

(f) indicating an attribute of said second location estimate as a function of a parameter of a subset of said NMRs.

1. A method of locating a mobile device comprising the steps of:

(a) providing a plurality of grid points in a geographic region;

(b) providing a plurality of network measurement reports for a mobile device in the geographic region;

(c) comparing ones of said plurality of grid points to at least one parameter of ones of said plurality of network measurement reports;

(d) generating a first location estimate of the mobile device for each of said ones of said plurality of network measurement reports (“

NMR”

); and

(e) determining a second location estimate of the mobile device as a function of at least one of said generated first location estimates.

2. The method of claim 1 further comprising the step of identifying and omitting outlier first location estimates by:

(i) determining a Mahalanobis distance from each first location estimate to the second location estimate;

(ii) determining a distance threshold from a median of the Mahalanobis distances multiplied by a predetermined factor; and

(iii) determining a third location estimate by averaging two or more of said first location estimates,

wherein first location estimates having a Mahalanobis distance to the second location estimate greater than the distance threshold are omitted from said determined third location estimate.

3. The method of claim 1 wherein determining a second location estimate further comprises averaging two or more first location estimates.

4. The method of claim 1 further comprising the step of omitting a first location estimate having an error estimate greater than a predetermined threshold.

5. The method of claim 1 wherein determining a second location estimate further comprises employing a weighted averaging of ones of said first location estimates.

6. The method of claim 1 wherein determining a second location estimate further comprises weighting a first location estimate by an inverse of a distance metric.

7. The method of claim 1 wherein determining a second location estimate further comprises normalizing a first location estimate by a sum of an inverse of a distance metric.

8. The method of claim 1 wherein determining a second location estimate further comprises weighting a first location estimate as a function of the number of reporting neighboring cells to a serving cell serving said mobile device.

9. The method of claim 1 further comprising the step of interpolating between ones of said plurality of grid points when more than one grid point corresponds to said at least one parameter of said plurality of network measurement reports.

10. The method of claim 9 wherein said interpolating is a function of the following relationship:

C_Ratio_i=[C_i−

C_min]/[C_N−

C_min]

for a set of N grid points, N>

1, where C_i is a metric associated with an i^th grid point, C_N is a metric associated with the worst corresponding grid point, and C_min is a metric associated with the best corresponding grid point.

11. The method of claim 10 wherein a grid point having a C_Ratio_i less than a predetermined threshold is a candidate grid point for said interpolating.

12. The method of claim 10 wherein a grid point having a distance from the best corresponding grid point less than a predetermined threshold is a candidate grid point for said interpolating.

13. The method of claim 9 wherein said interpolating is a function of the following relationship:

C_Ratio_i=[C_i−

C_min]/[C_min]

for a set of N grid points, N>

1, where C_i is a metric associated with an i^th grid point and C_min is a metric associated with the best corresponding grid point.

14. The method of claim 9 further comprising the step of assigning weights to the i^th grid point as a function of the following relationship:

15. The method of claim 1 wherein comparing ones of said plurality of grid points further comprises generating a distortion measure which is a function of a combination of parameters of ones of said plurality of network measurement reports and corresponding parameters of ones of said plurality of grid points.

16. The method of claim 15 wherein said distortion measure is selected from the group consisting of:

a Mahalanobis distance;

a comparison of received signal strengths of a serving cell and a neighboring cell between ones of said plurality of NMRs and grid points;

a comparison of received signal strengths of a neighboring cell between ones of said plurality of NMRs and grid points;

a comparison of received signal strengths of a first common neighboring cell and another neighboring cell between ones of said plurality of NMRs and grid points;

a comparison of average received signal strengths of reporting neighboring cells and received signal strengths of a neighboring cell between ones of said plurality of NMRs and grid points;

a comparison of average received signal strengths of serving and reporting neighbor cells received signal strengths of a selected neighbor or serving cell between ones of said plurality of NMRs and grid points; and

any combination thereof.

17. The method of claim 1 further comprising the step of providing a default location for said second location estimate if a second location estimate cannot be determined as a function of at least one of said generated first location estimates.

18. The method of claim 17 wherein said default location is a predetermined distance from a serving cell serving said mobile device at a heading along a serving sector azimuth.

19. The method of claim 17 wherein said default location is a function of timing advance or round trip time.

20. The method of claim 19 wherein said default location is an approximate range estimate from a serving cell serving said mobile device.

21. The method of claim 17 wherein said default location is a region determined as a function of a time difference of arrival (“

TDOA”

) measurement.

22. The method of claim 21 wherein said region is the intersection of a hyperbola defined by said TDOA with a timing advance region applicable to a serving cell serving said mobile device.

23. The method of claim 17 wherein said default location is determined as a function of a priori knowledge of sector coverage density.

25. The method of claim 24 wherein determining a second location estimate further comprises clustering the set of network measurement reports without reference to parameters in any of said subset of network measurement reports.

26. The method of claim 24 wherein the second location estimate is determined as the centroid of a region containing the tightest cluster of NMRs in said set of NMRs, said cluster having an aggregate measure higher than a predetermined threshold.

27. The method of claim 24 wherein the second location estimate is determined as the centroid of ones of said plurality of grid points occurring most often in said set of NMRs.

28. The method of claim 24 wherein determining a second location estimate further comprises clustering a subset of NMRs as a function of power of neighboring cells.

29. The method of claim 24 wherein determining a second location estimate further comprises clustering a subset of NMRs as a function of power of neighboring cells and another parameter in said subset.

30. The method of claim 24 wherein determining a second location estimate further comprises averaging two or more first location estimates.

31. The method of claim 24 further comprising the step of omitting a first location estimate having an error estimate greater than a predetermined threshold.

32. The method of claim 24 wherein determining a second location estimate further comprises employing a weighted averaging of ones of said first location estimates.

33. The method of claim 24 wherein determining a second location estimate further comprises weighting a first location estimate by an inverse of a distance metric.

34. The method of claim 24 wherein determining a second location estimate further comprises normalizing a first location estimate by a sum of an inverse of a distance metric.

35. The method of claim 24 wherein determining a second location estimate further comprises weighting a first location estimate as a function of the number of reporting neighboring cells to a serving cell serving said mobile device.

36. The method of claim 24 further comprising the step of identifying and omitting outlier first location estimates by:

(i) determining a Mahalanobis distance from each first location estimate to the second location estimate;

(ii) determining a distance threshold from a median of the Mahalanobis distances multiplied by a predetermined factor; and

(iii) determining a third location estimate by averaging two or more of said first location estimates,

wherein first location estimates having a Mahalanobis distance to the second location estimate greater than the distance threshold are omitted from said determined third location estimate.

37. The method of claim 24 wherein the step of indicating an attribute of said second location estimate further comprises determining an error estimate as an average of distances from each first location estimate to said second location estimate.

38. The method of claim 37 wherein said error estimate is determined as a function of the following relationship:

39. The method of claim 38 wherein said weighting factors are selected from the group consisting of:

probabilities determined during the first location estimate generation, probabilities determined during the second location estimate determination, distortion function values determined during the first location estimate generation, distortion function values determined during the second location estimate determination, and combinations thereof.

40. The method of claim 24 wherein said attribute is determined as a function of a measure selected from the group consisting of:

a fraction of first location estimates corresponding with the second location estimate;

a fraction of total first location estimates that lie within a predetermined distance of the second location estimate.

41. The method of claim 24 wherein determining a second location estimate further comprises weighting a first location estimate as a function of said attribute.

42. The method of claim 41 wherein said weighting is selected from the group consisting of:

a Mahalanobis distance, a probability density function.

43. The method of claim 24 further comprising the step of providing a default location for said second location estimate if said attribute is less than a predetermined threshold.

44. The method of claim 43 wherein said default location is a predetermined distance from a serving cell serving said mobile device at a heading along a serving sector azimuth.

45. The method of claim 43 wherein said default location is a function of timing advance or round trip time.

46. The method of claim 45 wherein said default location is an approximate range estimate from a serving cell serving said mobile device.

47. The method of claim 43 wherein said default location is a region determined as a function of a time difference of arrival (“

TDOA”

) measurement.

48. The method of claim 47 wherein said region is the intersection of a hyperbola defined by said TDOA with a timing advance region applicable to a serving cell serving said mobile device.

49. The method of claim 43 wherein said default location is determined as a function of a priori knowledge of sector coverage density.

38-1. The method of claim 37 wherein said error estimate is determined as a function of the following relationship: