Method and apparatus for GPS positioning, filtering and integration

• US 5,740,048 A
• Filed: 08/07/1992
• Issued: 04/14/1998
• Est. Priority Date: 08/07/1992
• Status: Expired due to Fees

• Current Assignee: James W. Chaffee, Jonathan S. Abel
• Original Assignee: James W. Chaffee, Jonathan S. Abel
• Inventors: Chaffee, James W., Abel, Jonathan S.
• Primary Examiner(s): Teska, Kevin J.
• Assistant Examiner(s): WALDER, STEPHEN JOSEPH
• Application Number:
  US07/927,936
• Time in Patent Office: 2,076 Days
• Field of Search: 364/449, 364/452, 364/453, 364/454, 364/449.7, 342/357, 342/458, 342/352, 375/81
• US Class Current: 701/200
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/49 whereby the further system is an inertial position system, e.g. loosely-coupled
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering

Abstract

A method and apparatus for processing data from a positioning or navigation system, such as global positioning system (GPS) navigation system data. In preferred embodiments, the invention is a method and apparatus for estimating position based on GPS measurements and optionally, measurements from other types of navigation sensors combined with GPS navigation system data. The apparatus of the invention includes a point position estimator (which preferably approximates a maximum likelihood estimator) for processing data from a positioning or navigation system to generate point position estimate data, and a smoothing filter for smoothing the point position estimate data. The smoothing filter can be a linear Kalman filter.

Claims

1. A two-stage filter for processing navigation data, including:

a navigation processor for computing point position and/or bias estimates from input pseudorange data; and

a smoothing filter for receiving and smoothing the point position and/or bias estimates output from the navigation processor.

2. The filter of claim 1, wherein the smoothing filter includes two separate filters for separately smoothing point position and bias estimates.

3. The filter of claim 1, wherein the smoothing filter smooths point position and bias estimates in parallel, in a manner in which cross-correlation between position and bias estimates is determined.

4. The filter of claim 1, wherein the smoothing filter is a constant-gain Kalman filter.

5. The filter of claim 1, wherein the smoothing filter is a time-varying-gain Kalman filter.

6. The filter of claim 1, wherein the smoothing filter is a linear smoothing filter.

7. The filter of claim 1, wherein the navigation processor receives and processes externally measured bias data, to improve quality of the point position and/or bias estimates.

8. The filter of claim 1, wherein the navigation processor computes measured bias data from the input pseudorange data, by filtering point bias estimates.

9. The filter of claim 1, wherein the navigation processor computes point position estimates based on pseudorange differences.

10. The filter of claim 1, wherein the navigation processor computes point position and/or bias estimates by minimizing or approximately minimizing a weighted norm of one of a pseudorange equation error and a pseudorange difference equation error.

11. The filter of claim 10, wherein the navigation processor performs approximate minimization using a divide and conquer method wherein estimates based on subsets of available pseudorange data are combined to form a unified estimate.

12. The filter of claim 10, wherein the navigation processor performs approximate minimization using a Lagrange multiplier technique wherein an optimum position is a function of a range parameter, wherein the range parameter satisfies a norm constraint.

13. The filter of claim 12, including means for computing the range parameter using a singular value decomposition method satisfying the norm constraint.

14. The filter of claim 1, wherein externally available point estimates of position, velocity, and/or acceleration coordinates are applied to the smoothing filter along with the navigation processor output to improve accuracy of the output of said smoothing filter.

15. The filter of claim 14, wherein the externally available point estimates are based on inertial navigation system data.

16. The filter of claim 14, wherein the externally available point estimates are based on LORAN time differences.

17. The filter of claim 14, wherein the externally available point estimates are based on transponder ranges.

18. The filter of claim 14, wherein the externally available point estimates are based on altimeter data.

19. The filter of claim 14, wherein the externally available point estimates are based on dead reckoning data.

20. The filter of claim 1, wherein the navigation processor receives and processes externally measured clock bias data to improve quality of the point position and/or bias estimates.

21. The filter of claim 20, wherein the externally measured clock bias data are formed by filtering point bias estimates computed from the input pseudorange data.

22. The filter of claim 20, wherein an externally measured position coordinate is used by the navigation processor to improve accuracy of the point position and/or bias estimates output from the navigation processor.

23. The filter of claim 22, wherein a gauge of the accuracy of a measured position coordinate is used to weight the contribution of the measured position coordinate to the point position and/or bias estimates.

24. The filter of claim 1, wherein available differential GPS corrections are used by the navigation processor to correct a pseudorange difference part of the pseudorange data.

25. The filter of claim 1, wherein the navigation processor computes estimates of covariance of the point position and/or bias estimates for use in the smoothing filter.

26. The filter of claim 1, wherein an externally available position coordinate is used to constrain the navigation processor output, thereby improving quality of the point position and/or bias estimates output from the navigation processor.

27. A two-stage filter for processing navigation data, including:

a navigation processor for computing point position estimates from input range data; and

a smoothing filter for receiving and smoothing the point position estimates output from the navigation processor.

28. A two-stage filter for processing navigation data, including:

a navigation processor for computing point position estimates from input range difference data; and

a smoothing filter for receiving and smoothing the point position estimates output from the navigation processor.

29. A method for processing navigation data, including the steps of:

(a) computing point position and/or bias estimates from input pseudorange data; and

(b) after step (a), smoothing the point position and/or bias estimates.

30. The method of claim 29, wherein step (b) includes the step of smoothing the point position and/or bias estimates in a Kalman filter.

31. The method of claim 29, wherein step (a) includes the step of approximately minimizing a weighted norm of one of a pseudorange equation error and a pseudorange difference equation error.

32. The method of claim 29, wherein step (a) includes the step of computing an estimate of covariance of the point position and/or bias estimates.