Track model constraint for GPS position

• US 20020107636A1
• Filed: 06/22/2001
• Published: 08/08/2002
• Est. Priority Date: 06/23/2000
• Status: Active Grant

• Current Assignee: Sportsmedia Technology Corporation
• Original Assignee: Sportvision, Inc. (Sportsmedia Technology Corporation)
• Inventors: Fenton, Patrick C., Honey, Stanley K., Ford, Thomas J., McGuffin, James O.
• Granted Patent: US 6,728,637 B2
• Time in Patent Office: Days
• US Class Current: 701/213
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01S 19/04 providing carrier phase data
• CPC Class Codes: G01S 19/50 whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks
• CPC Class Codes: G01S 5/0027 of actual mobile position, i.e. position determined on mobile
• CPC Class Codes: G01S 5/0036 of measured values, i.e. measurement on mobile and position calculation on base station
• CPC Class Codes: G01S 5/0054 of actual mobile position, i.e. position calculation on base station

Abstract

A track model is created for use with a GPS receiver. In one embodiment, the track model is a set of planar surfaces which approximate the contiguous surface on which navigation takes place. The GPS receiver searches for an appropriate planar surface associated with its approximate position. Having found the appropriate planar surface, the GPS receiver constrains its position using the planar surface associated with its approximate position. Using the track model improves the accuracy of the computed position at the time and improves the ambiguity estimation process so that positions with greatly improved accuracy are available sooner.

Claims

1. A method for constraining GPS derived position information for an object, comprising the steps of:

accessing a model of one or more navigation surfaces, said object travels in relation to said one or more navigation surfaces, said model includes a set of two or more model surfaces; and

using said model to constrain a GPS based determination of a position of said object.

2. A method according to claim 1, wherein:

said model surfaces are planar surfaces approximating said one or more navigation surfaces.

3. A method according to claim 2, wherein:

said step of using said model includes constraining said position using one of said planar surfaces.

4. A method according to claim 2, wherein:

said step of using said model includes identifying one of said planar surfaces as being in proximity to said object and constraining said position to said one of said planar surfaces.

5. A method according to claim 2, wherein:

said model is created based on a geographic frame;

said GPS based determination is performed in an ECEF frame; and

said model is transformed to an intermediate frame.

6. A method according to claim 2, wherein:

said planar surfaces are polygons.

7. A method according to claim 6, wherein:

said step of using said model includes identifying one of said polygons as being in proximity to said object and constraining said position to said one of said polygons.

8. A method according to claim 7, wherein:

said step of constraining said position to said one of said polygons includes taking into account a known height of a GPS antenna mounted to said object.

9. A method according to claim 7, wherein:

said step of constraining assumes that an antenna mounted to said object has a constant position in a direction normal to said one of said polygons.

10. A method according to claim 7, wherein:

said model is divided into a grid of rectangles; and

said step of identifying one of said polygons includes using an initial position to identify a first rectangle from said grid of rectangles and considering only polygons in said first rectangle in order to identify said one of said polygons.

11. A method according to claim 1, wherein:

said model is created based on a geographic frame;

said GPS based determination is performed in an ECEF frame; and

said model is transformed to an intermediate frame.

12. A method according to claim 1, wherein:

said step of using includes performing a single epoch least squares process.

13. A method according to claim 12, wherein:

said step of using includes constraining said least squares process based on said model.

14. A method according to claim 13, wherein:

said step of using includes using a Kalman filter to generate one or more estimates of a relative position between a reference receiver and said object.

15. A method according to claim 14, wherein:

said step of using includes constraining said Kalman filter based on said model.

16. A method according to claim 1, wherein:

said model surfaces are planar surfaces approximating said one or more navigation surfaces;

said model is created based on a geographic frame;

said GPS based determination is performed in an ECEF frame;

said model is transformed to an intermediate frame;

said planar surfaces are triangles;

said step of using said model includes identifying one of said triangles as being in proximity to said object and constraining said position to said one of said triangles;

said step of constraining assumes that an antenna mounted to said object has a constant position in a direction normal to said one of said triangles; and

said step of using includes performing a single epoch least squares process that is constrained by said one of said triangles.

17. A method according to claim 1, further comprising the steps of:

receiving satellite signals;

determining pseudoranges;

calculating an initial position, said initial position used by said step of using said model to constrain a GPS based determination; and

reporting said position.

18. A method according to claim 1, further comprising the step of:

creating said model, said step of creating said model includes the steps of;

surveying locations on or near said one or more navigation surfaces;

capturing aerial photographs of said one or more surfaces using a sensor;

recording locations of said sensor while capturing said aerial photographs; and

determining three dimensional coordinates on said one or more navigation surfaces using based on said captured aerial photographs and said recorded locations.

19. A method according to claim 18, wherein said step of creating said model further comprises the step of:

dividing said model into a plurality of polygons.

20. A method according to claim 19, wherein:

said step of using said model includes identifying one of said polygons as being in proximity to said object and constraining said position to said one of said polygons.

21. A method according to claim 18, wherein said step of creating said model further comprises the steps of:

extracting edges of said one or more surfaces; and

using said edges to divide said model into a plurality of polygons.

22. A method according to claim 1, wherein:

said model surfaces are planar surfaces; and

said planar surfaces are not parallel to a local level plane.

23. A method according to claim 1, wherein:

said step of using said model makes use of a constraint position that changes at almost every positioning epoch.

24. A method according to claim 1, wherein:

said step of using said model makes use of a covariance matrix that changes at almost every positioning epoch.

25. A method according to claim 1, wherein:

said step of using includes constraining a Kalman filter based on said model.

27. One or more processor readable storage devices according to claim 26, wherein:

said model surfaces are planar surfaces approximating said one or more navigation surfaces ;

and

said step of using said model includes identifying one of said planar surfaces as being in proximity to said object and constraining said position to said one of said planar surfaces.

28. One or more processor readable storage devices according to claim 27, wherein:

said model is created based on a geographic frame;

said GPS based determination is performed in an ECEF frame; and

said model is transformed to an intermediate frame.

29. One or more processor readable storage devices according to claim 28, wherein:

said planar surfaces are polygons.

30. One or more processor readable storage devices according to claim 29, wherein:

said model is divided into a grid of rectangles; and

said step of identifying one of said set of planar surfaces includes using an initial position to identify a first rectangle from said grid of rectangles and considering only polygons in said first rectangle in order to identify said one of said polygons.

31. One or more processor readable storage devices according to claim 26, wherein:

said step of using includes constraining a least squares process based on said model.

32. One or more processor readable storage devices according to claim 26, wherein:

said step of using includes constraining a Kalman filter based on said model.

33. One or more processor readable storage devices according to claim 26, wherein:

said model surfaces are planar surfaces approximating said one or more surfaces;

said model is created based on a geographic frame;

said GPS based determination is performed in an ECEF frame;

said model is transformed to an intermediate frame;

said planar surfaces are triangles;

said step of using said model includes identifying one of said triangles as being in proximity to said object and constraining said position to said one of said triangles;

said step of constraining assumes that an antenna mounted to said object has a constant position in a direction normal to said one of said triangles; and

said step of using includes performing a Kalman filter that is constrained by said one of said triangles.

34. One or more processor readable storage devices according to claim 26, wherein:

said model surfaces are planar surfaces; and

said planar surfaces are not parallel to a local level plane.

35. One or more processor readable storage devices according to claim 26, wherein:

said step of using said model makes use of a constraint position that changes at almost every positioning epoch.

36. One or more processor readable storage devices according to claim 26, wherein:

said step of using said model makes use of a covariance matrix that changes at almost every positioning epoch.

38. An apparatus according to claim 37, wherein:

said one or more processing units include an analog-to-digital converter, a signal processor, memory, a central processing unit, control and configuration logic, and an I/O interface.

39. An apparatus according to claim 37, wherein:

said one or more inputs include an antenna and a data input, said data input is capable of receiving differential GPS data; and

said one or more processor utilize said differential GPS data to determine a position of said object.

40. An apparatus according to claim 37, wherein:

said model surfaces are planar surfaces approximating said one or more navigation surfaces; and

said one or more processors identify one of said planar surfaces as being in proximity to said object and constraining said position to said one of said planar surfaces.

41. An apparatus according to claim 40, wherein:

said model is created based on a geographic frame;

said GPS based determination is performed in an ECEF frame; and

said model is transformed to an intermediate frame.

42. An apparatus according to claim 40, wherein:

said planar surfaces are polygons.

43. An apparatus according to claim 42, wherein:

said model is divided into a grid of rectangles; and

said one or more processors identify one of said planar surfaces by using an initial position to identify a first rectangle from said grid of rectangles and consider only polygons in said first rectangle in order to identify said one of said polygons.

44. An apparatus according to claim 43, wherein:

said one or more processor constrain a least squares process based on said model.

45. An apparatus according to claim 43, wherein:

said one or more processor constrain a Kalman filter based on said model.

46. An apparatus according to claim 37, wherein:

said model surfaces are planar surfaces approximating said one or more navigation surfaces;

said planar surfaces are triangles; and

said one or more processors identify one of said triangles as being in proximity to said object and constrain a least squares process based on said one of said triangles.

47. An apparatus according to claim 37, wherein:

said model surfaces are planar surfaces; and

said planar surfaces are not parallel to a local level plane.

48. An apparatus according to claim 37, wherein:

said one or more processing units use said model with a constraint position that changes at almost every positioning epoch.

49. An apparatus according to claim 37, wherein:

said one or more processing units use said model with a covariance matrix that changes at almost every positioning epoch.

26. One or more processor readable storage devices for storing processor readable code, said processor readable code for programming one or more processors to perform a method for constraining GPS derived position information for an object, the method comprising the steps of:

accessing a model of one or more navigation surfaces, said object travels in relation to said one or more navigation surfaces, said model includes a set of two or more model surfaces; and

using said model to constrain a GPS based determination of a position of said object.

37. An apparatus capable of constraining GPS derived position information for an object, comprising:

one or more inputs, said one or more inputs receive GPS data; and

one or more processing units, said one or more processing units access a model of one or more navigation surfaces and use said model to constrain a GPS based determination of a position of said object, said object travels on said one or more surfaces, said model includes two or more model surfaces.

50. A method for constraining GPS derived position information for an object, comprising the steps of:

accessing a model of height information;

constraining the derived height by accessing said model of height information, said height information is accessed from said model based on horizontal position information, computed based on GPS.

51. A method according to claim 50, wherein:

said model of height information includes 2 or more plainer surfaces approximating a navigation surface

52. A method according to claim 51, wherein:

said plainer surfaces are defined by their orientation in space with respect to a reference system and boundary definitions.

53. A method according to claim 52, wherein:

said plainer surfaces are triangles.

54. A method according to claim 50, wherein:

said model includes height and surface slope information arranged in two dimensions.