GNSS Signal Processing Methods and Apparatus with Ambiguity Selection

• US 20110140959A1
• Filed: 08/05/2009
• Published: 06/16/2011
• Est. Priority Date: 08/19/2008
• Status: Active Grant

• Current Assignee: Trimble Navigation Limited (Trimble Inc.)
• Original Assignee: Trimble Navigation Limited (Trimble Inc.)
• Inventors: Vollath, Ulrich
• Granted Patent: US 8,830,121 B2
• Time in Patent Office: Days
• US Class Current: 342/357.27
• CPC Class Codes: G01S 19/44 Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method

Abstract

Methods and apparatus are provided for estimating parameters, i.e. ambiguities, derived from GNSS signals. Observations of a GNSS signal from each of a plurality of GNSS satellites are obtained (4120). The observations are fed to a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals, each float ambiguity constituting a real number estimate associated with an integer number of wavelengths of the GNSS signal between a receiver of the GNSS signal and the GNSS satellite from which it is received, and the filter being for estimating a float value for each float ambiguity of the state vector (4140). A subset of float ambiguities of the state vector is selected (4150). Integer values are assigned to the estimated float values of the float ambiguities of the subset to define a plurality of integer ambiguity candidate sets (4160). A quality measure is determined for each of the candidate sets. A weighted average of the candidate sets is formed (4200). Ambiguities of the weighted average can be used in subsequent operations to aid in determining a position of the receiver or can be used to prepare data, e.g., in a network processor that can be used to augment position information of a rover.

Claims

1. Method to estimate parameters derived from global navigational satellite system (GNSS) signals useful to determine a position, comprising

obtaining observations of a GNSS signal from each of a plurality of GNSS satellites;

feeding the observations to a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals, each float ambiguity constituting a real number estimate associated with an integer number of wavelengths of the GNSS signal between a receiver of the GNSS signal and the GNSS satellite from which it is received, and the filter being for estimating a float value for each float ambiguity of the state vector;

selecting a subset of the ambiguities of the state vector;

assigning integer values to the estimated float values of the float ambiguities of the subset to define a plurality of candidate sets;

determining a quality measure for each of the candidate sets; and

forming a weighted average of the candidate sets.

2. The method of claim 1, wherein selecting a subset of float ambiguities of the state vector comprises one of:

(a) operating the filter to estimate from the observations a complete set of float ambiguity values and subsequently selecting the subset of float ambiguities from among the complete set of float ambiguity values, and (b) operating the filter to estimate from the observations a selected partial set of float ambiguity values comprising fewer than the complete set of float ambiguity values, wherein the partial set of float ambiguity values comprises the subset of float ambiguities of the state vector.

3. Method of claim 1, wherein the selecting comprises selecting, as float ambiguities of the subset, the float ambiguities corresponding to frequencies of satellites tracked continuously for the longest period of time.

4. Method of at claim 1, wherein forming the weighted average comprises

selecting a first number of candidate sets having a quality measure better than a first threshold, wherein the first threshold is determined based on a reference quality measure of a reference candidate set;

and

forming a weighted average of the selected candidate sets, each candidate set weighted in the weighted average based on its quality measure.

5. Method of claim 1, further comprising:

determining a problem in the floating solution by at least one of (1) comparing a quality measure of a reference candidate set with a problem threshold and (2) determining an improvement factor by comparing the quality measure of the reference candidate set with a quality measure of a superset of the float ambiguities of the reference candidate set, and

resetting the estimated float value of at least one of the float ambiguities which is not included in the candidate set.

6. Method of claim 1, comprising using the weighted average to estimate a position of the receiver of the GNSS signals.

7. Method of claim 4, wherein the reference candidate set is the candidate set having the best quality measure.

8. Method of claim 4, wherein the quality measure of the candidate sets is constituted by a residual error norm value, the residual error norm value of a candidate set being a measure for a statistical distance of the candidate set to the state vector having the float ambiguities.

9. Method of claim 4, wherein the first threshold is determined as at least one of a fraction of, a multiple of, and a distance to the reference quality measure.

10. Method of claim 4, comprising, if the first number of selected candidate sets is smaller than a second threshold, selecting, for forming the weighted average, a second number of further candidate sets on the basis of the quality measures of the candidate sets in decreasing order starting with the non-selected candidate set having the best quality measure, the second number being constituted by the difference between the first number of selected candidate sets and a second threshold defining a minimum number of candidate sets to be included in the weighted average.

11. Method of claim 4 comprising, if the first number of selected candidate sets is larger than a third threshold, excluding a third number of selected candidate sets from forming the weighted average in decreasing order starting with the selected candidate set having the worst quality measure, wherein the third number is constituted by the difference between the first number of selected candidate sets and a third threshold defining a maximum number of candidate sets to be included in the weighted average.

12. Method of at claim 1, wherein forming the weighted average comprises:

determining the best quality measure of the candidate sets;

determining an expectation value of the candidate set having the best quality measure;

determining an error measure as a ratio of the best quality measure to the expectation value;

adapting the quality measures of the candidate sets as a function of the error measure; and

performing a selection of a first number of candidate sets based on the adapted quality measures.

13. Method of claim 1, wherein forming the weighted average comprises:

determining the best quality measure of the candidate sets;

determining an expectation value of the candidate set having the best quality measure;

determining an error measure as a ratio of the best quality measure to the expectation value;

adapting the quality measures of the candidate sets as a function of the error measure; and

forming the weighted average based on the adapted quality measure.

14. Method of at claim 12, comprising adapting the quality measures of the candidate sets as a function of the error measure by adjusting the variance-covariance matrix of the float solution.

15. Method of at claim 12, comprising adjusting a variance-covariance matrix of the filter using the error measure, if the error measure is in a predetermined range.

16. Method of claim 12, comprising adjusting a variance-covariance matrix of the filter using the error measure, if the error measure is larger than one.

17. Method of at least one of claims 1 to 16 claim 1, comprising

estimating by the filter a float value for each float ambiguity of the state vector and covariance values associated with the state vector;

determining a formal precision value based on covariance values derived of the filter, the formal precision value being a measure for an achievable precision;

determining an achieved precision value of the weighted average;

comparing the achieved precision value with the formal precision value to obtain a convergence value; and

indicating a convergence of the determination of the state vector based on the convergence value.

18. Method of claim 17, wherein the convergence value is obtained as a ratio of the achieved precision value to the formal precision value.

19. Method of at claim 17, comprising

determining an instance in time when the convergence value of the position is better than a convergence threshold;

and

indicating a convergence of the determination of the state vector at and after the determined instance in time.

20. Method of at claim 17, comprising

estimating an achieved precision of a position of the receiver determined based on the weighted ambiguities;

and

indicating a convergence of the determination of the state vector, if the achieved precision of the position is better than an inclusion threshold.

21. Method of claim 17, comprising

estimating an achieved precision of a position of the receiver

determined based on the weighted ambiguities;

and

indicating a non-convergence of the determination of the state vector, if the achieved precision of the position is worse than an exclusion threshold.

22. Method of claim 1, comprising:

obtaining observations of the at least one frequency of the GNSS signals from the plurality of GNSS satellites to obtain observations at a plurality of instances in time;

updating the float ambiguities of the state vector over time on the basis of the observations;

determining that an interruption in tracking of at least one signal of a satellite occurred; and

maintaining the float ambiguity of the state vector for the at least one signal for which an interruption in tracking occurred at the value before the interruption in tracking occurred.

23. Method of claim 22, wherein it is determined that an interruption in tracking of at least one signal of a satellite occurred if an observation for the at least one signal is not available for at least one of the instances in time.

24. Method of claim 22, wherein it is determined that an interruption in tracking of at least one signal of a satellite occurred, if a cycle slip occurred.

25. Method of claim 22 comprising, if, after an interruption in tracking of a signal, tracking of the signal resumes, maintaining the float ambiguity of the state vector for the signal for which an interruption in tracking occurred at the value before the interruption in tracking occurred as a first float ambiguity and introducing into the state vector a second float ambiguity for the signal after resuming tracking.

54. Computer-readable physical storage medium embodying instructions configured, when executed on a computer processing unit, to carry out the method of claim 1.

26. Apparatus to estimate parameters derived from global navigational satellite system (GNSS) signals useful to determine a position, comprising

a receiver adapted to obtain observations of a GNSS signal from each of a plurality of GNSS satellites;

a filter having a state vector at least comprising a float ambiguity for each received frequency of the GNSS signals, each float ambiguity constituting a real number estimate associated with an integer number of wavelengths of the GNSS signal between a receiver of the GNSS signal and the GNSS satellite from which it is received, and the filter being for estimating a float value for each float ambiguity of the state vector;

a processing element adapted to

select a subset of float ambiguities of the state vector;

assign integer values to the estimated float values of the float ambiguities of the subset to define a plurality of integer ambiguity candidate sets;

determine a quality measure for each of the candidate sets; and

form a weighted average of the candidate sets.

27. Apparatus of claim 26, wherein the processing is adapted to select a subset of float ambiguities of the state vector by one of:

(a) operating the filter to estimate from the observations a complete set of float ambiguity values and subsequently selecting the subset of float ambiguities from among the complete set of float ambiguity values, and (b) operating the filter to estimate from the observations a selected partial set of float ambiguity values comprising fewer than the full set of float ambiguity values, wherein the partial set of float ambiguity values comprises the subset of float ambiguities of the state vector.

28. Apparatus of claim 26, wherein the processing element is adapted to select, as float ambiguities of the subset, the float ambiguities corresponding to frequencies of satellites tracked continuously for the longest period of time.

29. Apparatus of claim 26, wherein the processing element is adapted to, in order to form the weighted average,

select a first number of candidate sets having a quality measure better than a first threshold, wherein the first threshold is determined based on a reference quality measure a reference candidate set;

and

form a weighted average of the selected candidate sets, each candidate set weighted in the weighted average based on its quality measure.

30. Apparatus of claim 26, wherein the processing element is further adapted to:

determine a problem in the floating solution by at least one of (1) comparing a quality measure of a reference candidate set with a problem threshold and (2) determining an improvement factor by comparing the quality measure of the reference candidate set with a quality measure of a superset of the float ambiguities of the reference candidate set, and

reset the estimated float value of at least one of the float ambiguities which is not included in the candidate set.

31. Apparatus of claim 26, wherein the processing element is adapted to use the weighted average to estimate a position of the receiver of the GNSS signals.

32. Apparatus of claim 29, wherein the reference candidate set is the candidate set having the best quality measure.

33. Apparatus of claim 29, wherein the quality measure of the candidate sets is constituted by a residual error norm value, the residual error norm value of a candidate set being a measure for a statistical distance of the candidate set to the state vector having the float ambiguities.

34. Apparatus of claim 29, wherein the processing element is adapted to determine the first threshold as at least one of a fraction of, a multiple of, and a distance to the reference quality measure.

35. Apparatus of claim 29, wherein the processing element is adapted to, if the first number of selected candidate sets is smaller than a second threshold, select, for forming the weighted average, a second number of further candidate sets on the basis of the quality measures of the candidate sets in decreasing order starting with the non-selected candidate set having the best quality measure, the second number being constituted by the difference between the first number of selected candidate sets and a second threshold defining a minimum number of candidate sets to be included in the weighted average.

36. Apparatus of claim 29, wherein the processing element is adapted to, if the first number of selected candidate sets is larger than a third threshold, exclude a third number of selected candidate sets from forming the weighted average in decreasing order starting with the selected candidate set having the worst quality measure, wherein the third number is constituted by the difference between the first number of selected candidate sets and a third threshold defining a maximum number of candidate sets to be included in the weighted average.

37. Apparatus of claim 26, wherein the processing element is adapted to, in order to form the weighted average,

determine the best quality measure of the candidate sets;

determine an expectation value of the candidate set having the best quality measure;

determine an error measure as a ratio of the best quality measure to the expectation value;

adapt the quality measures of the candidate sets as a function of the error measure; and

perform a selection of a first number of candidate sets based on the adapted quality measures.

38. Apparatus of claim 26, wherein the processing element is adapted to, in order to form the weighted average,

determine the best quality measure of the candidate sets;

determine an expectation value of the candidate set having the best quality measure;

determine an error measure as a ratio of the best quality measure to the expectation value;

adapt the quality measures of the candidate sets as a function of the error measure; and

form the weighted average based on the adapted quality measure.

39. Apparatus of claim 37, wherein the processing element is adapted to adapt the quality measures of the candidate sets as a function of the error measure by adjusting the variance-covariance matrix of the float solution.

40. Apparatus of claim 37, wherein the processing element is adapted to adjust a variance-covariance matrix of the filter using the error measure, if the error measure is in a predetermined range.

41. Apparatus of claim 37, wherein the processing element is adapted to adjust a variance-covariance matrix of the filter using the error measure, if the error measure is larger than one.

42. Apparatus of claim 26, wherein the filter is adapted to estimate a float value for each float ambiguity of the state vector and covariance values associated with the state vector;

and the processing element is adapted to

determine a formal precision value based on covariance values derived of the filter, the formal precision value being a measure for an achievable precision;

determine an achieved precision value of the weighted average;

compare the achieved precision value with the formal precision value to obtain a convergence value; and

indicate a convergence of the determination of the state vector based on the convergence value.

43. Apparatus of claim 42, wherein the processing element is adapted to obtain the convergence value as a ratio of the achieved precision value to the formal precision value.

44. Apparatus of claim 42, wherein the processing element is adapted to

determine an instance in time when the convergence value of the position is better than a convergence threshold;

and

indicate a convergence of the determination of the state vector at and after the determined instance in time.

45. Apparatus of claim 42, wherein the processing element is adapted to

estimate an achieved precision of a position of the receiver determined based on the weighted ambiguities;

and

indicate a convergence of the determination of the state vector, if the achieved precision of the position is better than an inclusion threshold.

46. Apparatus of claim 42, wherein the processing element is adapted to

estimate an achieved precision of a position of the receiver determined based on the weighted ambiguities;

and

indicate a non-convergence of the determination of the state vector, if the achieved precision of the position is worse than an exclusion threshold.

47. Apparatus of claim 26, wherein the filter is adapted to obtain observations of the at least one frequency of the GNSS signals from the plurality of GNSS satellites to obtain observations at a plurality of instances in time;

and the processing element is adapted to

update the float ambiguities of the state vector over time on the basis of the observations;

determine that an interruption in tracking of at least one signal of a satellite occurred; and

maintain the float ambiguity of the state vector for the at least one signal for which an interruption in tracking occurred at the value before the interruption in tracking occurred.

48. Apparatus of claim 47, wherein the processing element is adapted to determine that an interruption in tracking of at least one signal of a satellite occurred if an observation for the at least one signal is not available for at least one of the instances in time.

49. Apparatus of claim 47, wherein the processing element is adapted to determine that an interruption in tracking of at least one signal of a satellite occurred, if a cycle slip occurred.

50. Apparatus of claim 47 wherein the processing element is adapted to, if, after an interruption in tracking of a signal, tracking of the signal resumes, maintain the float ambiguity of the state vector for the signal for which an interruption in tracking occurred at the value before the interruption in tracking occurred as a first float ambiguity and introducing into the state vector a second float ambiguity for the signal after resuming tracking.

51. Rover comprising an apparatus according to claim 26.

52. Network station comprising an apparatus according to claim 26.

53. (canceled)