Deeply-integrated adaptive GPS-based navigator with extended-range code tracking

• US 6,731,237 B2
• Filed: 12/03/2001
• Issued: 05/04/2004
• Est. Priority Date: 11/09/1999
• Status: Expired due to Term

• Current Assignee: Charles Stark Draper Laboratory Incorporated
• Original Assignee: Charles Stark Draper Laboratory Incorporated
• Inventors: Gustafson, Donald E., Elwell, John M. Jr., Dowdle, John R.
• Primary Examiner(s): Phan, Dao
• Application Number:
  US10/010,093
• Publication Number:
  US 20020126044A1
• Time in Patent Office: 883 Days
• Field of Search: 342/357.01, 342/357.06, 342/352, 342/357.12, 701/213, 701/215, 701/214, 701/217, 701/300
• US Class Current: 342/357.59
• CPC Class Codes: G01C 21/1652 with ranging devices, e.g. LIDAR or RADAR
• CPC Class Codes: G01S 19/21 Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
• CPC Class Codes: G01S 19/26 involving a sensor measurement for aiding acquisition or tracking
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

A system and method for generating a multidimensional navigation solution utilizes Global Positioning System (GPS) data to obtain highly reliable and accurate navigational solutions in high interference and dynamic environments, at a performance level which has been heretofore unattainable. Additional sensors such as inertial (gyros and accelerometers), altimeters, radars, etc. may be employed in a deeply integrated configuration. The approach taken in the present invention differs from that of previous techniques through its exploitation of nonlinear filtering methods; as a result, the navigation system architecture and processes employed yield significant improvements in navigation system performance, both in code tracking and reacquisition, and in carrier tracking and reacquisition. The improvements are particularly significant at low signal/noise ratios, where conventional approaches are especially susceptible to loss of code lock or carrier lock.

Claims

1. A system for determining a multidimensional navigation state for a navigation platform based on radio navigation data transmitted by external terminals comprising:

a radio navigation data receiver for receiving and amplifying said radio navigation data embedded in noise;

a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of a degree of correlation for each correlator between said radio navigation data and a known internal replica signal, said correlator output functions being non-triangular M code correlator output functions;

an estimator for estimating the radio navigation data signal power and noise power from the correlator output functions; and

a measurement update unit including a nonlinear filter, said measurement update unit for updating the navigation state based on conditional moments of the navigation state calculated as a function of the estimated radio navigation data signal power and noise power, and the correlator outputs.

2. The system of claim 1 further comprising an inertial sensor unit for generating inertial data indicative of the inertial behavior of the platform and wherein the conditional moments of the navigation state are further calculated as a function of the inertial data.

3. The system of claim 1 wherein the radio navigation data comprises Global Positioning System (GPS) data, and wherein the radio navigation data receiver comprises a GPS receiver.

4. The system of claim 1 further comprising a propagator for applying sensed inertial data to a dynamic model based on a recently updated navigation state to generate updated inertial data provided to said measurement update unit.

5. The system of claim 1 wherein the multidimensional navigation state includes information selected from the group consisting of position, velocity, attitude, receiver clock errors, inertial sensor errors, propagation delays, and satellite errors.

6. The system of claim 1 wherein the conditional moments are calculated using a set of correlator moments which are calculated with said nonlinear filter, and which represent statistical expectations of nonlinear functions of line-of-sight position error.

7. The system of claim 1 wherein the conditional moments are calculated to include measurement non-linearities in the correlator output functions.

8. The system of claim 1 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

9. The system of claim 1 wherein said estimator comprises:

a signal power estimator for determining radio navigation data signal power based on correlator moments; and

a noise power estimator for estimating noise power based on the correlator moments and said signal power estimate.

10. An estimator for estimating signal and noise power in a system for determining a multidimensional navigation state for a navigation platform based on radio navigation data embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output function indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said estimator comprising:

a signal power estimator for determining radio navigation data signal power based on correlator moments;

a noise power estimator for estimating noise power based on the correlator moments and said signal power estimate; and

a nonlinear filter for calculating said correlator moments, which represent statistical expectations of nonlinear functions of line-of-sight position errors, said correlator moments being calculated according to the equation;

$\begin{array}{c}{M}_{c,k}^{\left(s\right)}={\int <br><br>}_{-\mathrm{\infty <br><br>}}^{\mathrm{\infty <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{\tau <br><br>}<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^s<br><br>R_c^2<br><br>\left(\mathrm{\tau <br><br>}\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)<br><br><br><br>\mathrm{where}\\ \mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)=k<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}+\mathrm{\tau <br><br>}<br><br><br><br>\mathrm{and}\\ P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)=\frac{{\mathrm{<br><br>}}^{-\frac{{\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^2}{2<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}}{\sqrt{2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}.\end{array}$

11. The estimator of claim 10 further comprising a bias estimator for determining a bias estimate of each correlator in the bank, and wherein the signal power estimate and noise power estimate are further based on the bias estimate.

12. The estimator of claim 11 wherein the bias estimator comprises:

a smoothing function for updating the bias estimate of each correlator according to a smoothing time constant; and

a minimum function for determining the bias estimate as the minimum value of the smoothed bias estimates.

13. The estimator of claim 10 wherein said correlator moments comprise a first conditional moment comprising a conditional mean.

14. The estimator of claim 10 wherein the noise power estimate comprises a vector, such that an independent noise power estimate is calculated for each correlator in the correlator bank.

15. The estimator of claim 10 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

16. The estimator of claim 10 further comprising an inertial sensor unit for generating inertial data indicative of the inertial behavior of the platform and wherein the conditional moment of the navigation state is further calculated as a function of the inertial data.

17. The estimator of claim 10 wherein the radio navigation data comprises Global Positioning System (GPS) data, and wherein the radio navigation data receiver comprises a GPS receiver.

18. A measurement update unit for updating a navigation state and error covariances of the navigation state of a navigation platform based on radio navigation data transmitted by external terminals and embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said correlator output functions being non-triangular M code correlator output functions, said measurement update unit comprising:

a state update unit including a nonlinear filter, said state update unit for updating the navigation state as a function of the correlator output functions weighted by state gain functions, the state gain functions being based on a set of correlator moments, which are calculated with said nonlinear filter and which represent statistical expectations of nonlinear functions of line-of-sight position error, a propagation of a previous navigation state, and an error covariance matrix; and

an error covariance matrix update unit for updating the error covariances of the navigation state based on the correlator moments.

19. The measurement update unit of claim 18 wherein the propagation of the previous navigation state comprises an inertial data propagation.

20. The measurement update unit of claim 18 wherein the statistical expectations of nonlinear functions of the line-of-sight position error are functions of the error covariances of the navigation state and the noise.

21. The measurement update unit of claim 18 wherein the state gain functions and error covariances of the navigation state are further based on estimates of the radio navigation data signal power and noise power.

22. The measurement update unit of claim 18 wherein said radio navigation data signal power estimate and noise power estimate are calculated by an estimator comprising:

a signal power estimator for determining radio navigation data signal power based on the correlator moments; and

a noise power estimator for estimating noise power based on the correlator moments and said signal power estimate.

23. The measurement update unit of claim 18 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

24. The measurement update unit of claim 18 wherein the radio navigation data comprises Global Positioning System (GPS) data, and wherein the radio navigation data receiver comprises a GPS receiver.

25. The measurement update unit of claim 18 wherein the line-of-sight error comprises the difference between a measured and an estimated line-of-sight range between the receiver and the external terminals.

26. A measurement update unit for updating a navigation state and error covariances of the navigation state of a navigation platform based on radio navigation data carried on a radio navigation data carrier signal embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said correlator output functions being non-triangular M code correlator output functions, said bank of correlators including a prompt correlator for processing radio navigation data signals near the radio navigation data carrier frequency, said measurement update unit comprising:

a state update unit including a nonlinear filter, said state update unit for updating the navigation state as a function of the prompt correlator output function weighted by state gain functions, the state gain functions being based on a set of correlator moments which are calculated with said nonlinear filter and which represent statistical expectations of nonlinear functions of line-of-sight position error, and an error covariance matrix;

an error covariance matrix update unit for updating the error covariances of the navigation state based on the correlator moments; and

an integrity management unit for maintaining the integrity of the navigation state and the error covariances.

27. The measurement update unit of claim 26 wherein the state gain functions are further based on estimates of the radio navigation data carrier signal power and noise power, a data bit estimate, an inertial data propagation of a previous navigation state and an error covariance matrix.

28. The measurement update unit of claim 26 wherein the noise power is calculated based on the average noise power in at least two non-prompt correlator output functions.

29. The measurement update unit of claim 26 wherein the state update unit and error covariance matrix update unit operate linearly for a relatively large radio navigation data carrier signal power to noise power ratio and operate nonlinearly for a relatively small radio navigation data carrier signal power to noise power ratio.

30. The measurement update unit of claim 26 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

31. A measurement update unit for updating a navigation state and error covariances of the navigation state of a navigation platform based on radio navigation data embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said correlator output functions being non-triangular M code correlator output functions, said measurement update unit comprising:

a state update unit for updating the navigation state as a function of the correlator output functions weighted by state gain functions, the state gain functions being based on a set of correlator moments which are statistical expectations of nonlinear functions of line-of-sight position error variance, a propagation of a previous navigation state and an error covariance matrix;

an error covariance matrix update unit for updating the error covariances of the navigation state based on the correlator moments; and

an integrity management unit for maintaining the integrity of the navigation state and the error covariances.

32. The measurement update unit of claim 31 wherein the propagation of the previous navigation state comprises an inertial data propagation.

33. The measurement update unit of claim 31 wherein the state gain functions and error covariances of the navigation state are further based on estimates of the radio navigation data signal power and noise power.

34. The measurement update unit of claim 31 wherein the line-of-sight position error is a function of the error covariances of the navigation state and the noise, and wherein the integrity management unit monitors and limits the signal and noise power estimate values.

35. The measurement update unit of claim 31 wherein the integrity management unit limits growth rate of the error covariances.

36. The measurement update unit of claim 35 wherein the inertial data is sensed by inertial data sensors and wherein the growth of the error covariances are limited to an upper bound based on estimated accuracy of the inertial sensors.

37. The measurement update unit of claim 31 wherein the integrity management unit monitors and maintains the positive-definiteness of the error covariance matrix.

38. The measurement update unit of claim 31 wherein the integrity management unit limits estimated change of the navigation state.

39. A method for determining a multidimensional navigation state for a navigation platform based on radio navigation data transmitted by external terminals comprising:

receiving and amplifying said radio navigation data embedded in noise at a radio navigation data receiver;

extracting said radio navigation data from said noise over a range of delay error at a bank of correlators, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known internal replica signal, said correlator output functions being non-triangular M code correlator output functions;

estimating the radio navigation data signal power and noise power from the correlator output functions;

updating the navigation state based on the conditional moments of the navigation state calculated using a set of correlator moments as a function of the estimated radio navigation data signal power and noise power, and the correlator outputs;

calculating said correlator moments, which and represent statistical expectations of nonlinear functions of line-of-sight position errors, with a nonlinear filter.

40. The method of claim 39 further comprising generating inertial data indicative of the inertial behavior of the platform and wherein the conditional moments of the navigation state are further calculated as a function of the inertial data.

41. The method of claim 39 wherein the radio navigation data comprises Global Positioning System (GPS) data, and wherein the radio navigation data receiver comprises a GPS receiver.

42. The method of claim 39 further comprising applying a dynamical model to the navigation state to generate a propagated navigation state to be updated.

43. The method of claim 42 further comprising applying sensed inertial data indicative of the inertial behavior of the platform to the dynamical model.

44. The method of claim 39 further comprising updating the navigation state based on auxiliary sensor data.

45. The method of claim 44 wherein the auxiliary data is selected from the group of data types consisting of inertial data, altimeter data, radar data, and electro-optical device data.

46. The method of claim 39 wherein the multidimensional navigation state includes information selected from the group consisting of position, velocity, attitude, receiver clock errors, inertial sensor errors, propagation delays, and satellite errors.

47. The method of claim 39 wherein said conditional moments are calculated to include measurement non-linearities in the correlator output functions.

48. The method of claim 47 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

49. The method of claim 39 wherein estimating comprises:

estimating radio navigation data signal power based on the correlator moments; and

estimating noise power estimator for estimating noise power based on the correlator moments and said signal power estimate.

50. A method for estimating signal and noise power in a system for determining a multidimensional navigation state for a navigation platform based on radio navigation data embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, comprising:

calculating correlator moments, which and represent statistical expectations of nonlinear functions of line-of-sight position errors, with a nonlinear filter;

determining radio navigation data signal power based on the correlator moments; and

estimating noise power based on the correlator moments and said signal power estimate;

wherein said correlator moments are calculated according to the equation;

$\begin{array}{c}{M}_{c,k}^{\left(s\right)}={\int <br><br>}_{-\mathrm{\infty <br><br>}}^{\mathrm{\infty <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{\tau <br><br>}<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^s<br><br>R_c^2<br><br>\left(\mathrm{\tau <br><br>}\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)<br><br><br><br>\mathrm{where}\\ \mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)=k<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}+\mathrm{\tau <br><br>}<br><br><br><br>\mathrm{and}\\ P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)=\frac{{\mathrm{<br><br>}}^{-\frac{{\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^2}{2<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}}{\sqrt{2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}.\end{array}$

51. The method of claim 50 further comprising determining a bias estimate of each correlator in the bank, and wherein the signal power estimate and noise power estimate are further based on the bias estimate.

52. The method of claim 51 further comprising determining the bias estimate according to the steps of:

updating the bias estimate of each correlator according to a smoothing time constant; and

determining the bias estimate as the minimum value of the smoothed bias estimates.

53. The method of claim 50 wherein said correlator moments each comprise a first conditional moment comprising a conditional mean.

54. The method of claim 50 wherein the noise power estimate comprises a vector, such that an independent noise power estimate is calculated for each correlator in the correlator bank.

55. The method of claim 50 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

56. The method of claim 50 further comprising generating inertial data indicative of the inertial behavior of the platform and wherein the correlator moments are further calculated as a function of the inertial data.

57. The method of claim 50 wherein the radio navigation data comprises Global Positioning System (GPS) data, and wherein the radio navigation data receiver comprises a GPS receiver.

58. A method for updating a navigation state and error covariances of the navigation state of a navigation platform based on radio navigation data transmitted by external terminals and embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said correlator output functions being non-triangular M code correlator output functions, the method comprising:

calculating correlator moments, which represent statistical expectations of nonlinear functions of line-of sight position errors, a propagation of a previous navigation state and an error covariances matrix, with a nonlinear filter;

updating the navigation state as a function of the correlator output functions weighted by state gain functions, the state gain functions being based on a set of said correlator moments; and

updating the error covariances of the navigation state based on the correlator moments.

59. The method of claim 58 wherein the propagation of a previous navigation state comprises an inertial data propagation.

60. The method of claim 58 wherein the line-of-sight position error is a function of the error covariances of the navigation state and the noise.

61. The method of claim 58 wherein the state gain functions and error covariances of the navigation state are further based on estimates of the radio navigation data signal power and noise power.

62. The method of claim 58 wherein said radio navigation data signal power estimate and noise power estimate are calculated by:

estimating radio navigation data signal power based on the correlator moments; and

estimating noise power based on the correlator moments and said signal power estimate.

63. The method of claim 58 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

64. The method of claim 58 wherein the radio navigation data comprises Global Positioning System (GPS) data, and wherein the radio navigation data receiver comprises a GPS receiver.

65. The method of claim 58 wherein the line-of-sight error comprises the difference between a measured and an estimated line-of-sight range between the receiver and the external terminals.

66. A method for updating a navigation state and error covariances of the of a navigation platform based on radio navigation data carried on a radio navigation data carrier signal embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said correlator output functions being non-triangular M code correlator output functions, said bank of correlators including a prompt correlator for processing radio navigation data signals near the radio navigation data cater frequency, the method comprising:

updating the navigation state as a function of the prompt correlator output function weighted by state gain functions, the state gain functions being based on a set of correlator moments which are calculated with a nonlinear filter and which represent statistical expectations of nonlinear functions of line-of-sight position error, and an error covariance matrix; and

updating the error covariances of the navigation state based on said state gain functions.

67. The method of claim 66 wherein the state gain functions are further based on estimates of the radio navigation data carrier signal power and noise power, a data bit estimate and an error covariance matrix.

68. The method of claim 66 wherein the noise power is calculated based on the average noise power in at least two non-prompt correlator output functions.

69. The method of claim 66 wherein the state update unit and error covariance matrix update unit operate linearly for a relatively large radio navigation data carrier signal power to noise power ratio and operate non-linearly for a relatively small radio navigation data carrier signal power to noise power ratio.

70. The method of claim 66 wherein the bank of correlators comprises an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal.

71. A method for updating a navigation state and error covariances of the navigation state of a navigation platform based on radio navigation data embedded in noise, said radio navigation data signal being received by a radio navigation data receiver including a bank of correlators for extracting said radio navigation data from said noise over a range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator between said radio navigation data and a known replica signal, said correlator output functions being non-triangular M code correlator output functions, the method comprising:

updating the navigation state as a function of the correlator output functions weighted by state gain functions, the state gain functions being based on a set of correlator moments which are statistical expectations of nonlinear functions of line-of-sight position error, a propagation of a previous navigation state, and an error covariance matrix;

updating the error covariances of the navigation state based on the correlator moments; and

maintaining the integrity of the navigation state and the error covariances.

72. The method of claim 71 wherein the propagation of the previous navigation state comprises an inertial data propagation.

73. The method of claim 72 further comprising sensing the inertial data by inertial data sensors and limiting the growth of the error covariances to an upper bound based on estimated accuracy of the inertial sensors.

74. The method of claim 71 wherein the state gain functions and error covariances of the navigation state are further based on estimates of the radio navigation data signal power and noise power.

75. The method of claim 71 wherein the line-of-sight position error is a function of the error covariances of the navigation state and the noise, and wherein maintaining comprises monitoring and limiting the signal and noise power estimate values.

76. The method of claim 71 wherein maintaining the integrity further comprises limiting growth rate of the error covariances.

77. The method of claim 71 wherein maintaining integrity further comprises monitoring and maintaining the positive-definiteness of the error covariance matrix.

78. The method of claim 71 wherein maintaining integrity further comprises limiting estimated change of the navigation state.

79. A system for determining a multidimensional navigation state for a navigation platform based on radio navigation data transmitted by external terminals comprising:

a radio navigation data receiver for receiving and amplifying said radio navigation data embedded in noise;

an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of a degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal;

an estimator for estimating the radio navigation data signal power and noise power from the correlator output functions; and

a measurement update unit including a nonlinear filter, said measurement update unit for updating the navigation state based on a calculation of the conditional moments of the navigation state using a set of correlator moments, the calculation being based on the estimated radio navigation data signal power and noise power and the correlator output functions;

wherein said correlator moments are calculated with said nonlinear filter and represent statistical expectations of nonlinear functions of the line-of-sight position errors, said correlator moments being calculated according to the equation. $\begin{array}{c}{M}_{c,k}^{\left(s\right)}={\int <br><br>}_{-\mathrm{\infty <br><br>}}^{\mathrm{\infty <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{\tau <br><br>}<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^s<br><br>R_c^2<br><br>\left(\mathrm{\tau <br><br>}\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)<br><br><br><br>\mathrm{where}\\ \mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)=k<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}+\mathrm{\tau <br><br>}<br><br><br><br>\mathrm{and}\\ P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)=\frac{{\mathrm{<br><br>}}^{-\frac{{\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^2}{2<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}}{\sqrt{2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}.\end{array}$

80. A system for determining a multidimensional navigation state for a navigation platform based on radio navigation data transmitted by external terminals comprising:

a radio navigation data receiver for receiving and amplifying said radio navigation data embedded in noise;

an extended-range bank of correlators for extracting said radio navigation data from said noise over an extended range of delay error, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of a degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal;

an estimator for estimating the radio navigation data signal power and noise power from the correlator output functions; and

a measurement update unit for updating the navigation state based on a calculation of the conditional moments of the navigation state using a set of correlator moments, the calculation being based on the estimated radio navigation data signal power and noise power and the functions;

wherein said correlator moments represent statistical expectations of nonlinear functions of the line-of-sight position errors and are calculated according to the equation;

$\begin{array}{c}{M}_{c,k}^{\left(s\right)}={\int <br><br>}_{-\mathrm{\infty <br><br>}}^{\mathrm{\infty <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{\tau <br><br>}<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^s<br><br>R_c^2<br><br>\left(\mathrm{\tau <br><br>}\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)<br><br><br><br>\mathrm{where}\\ \mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)=k<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}+\mathrm{\tau <br><br>}<br><br><br><br>\mathrm{and}\\ P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)=\frac{{\mathrm{<br><br>}}^{-\frac{{\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^2}{2<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}}{\sqrt{2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}.\end{array}$

81. A method for determining a multidimensional navigation state for a navigation platform based on radio navigation data transmitted by external terminals comprising:

receiving and amplifying said radio navigation data embedded in noise at a radio navigation data receiver;

extracting said radio navigation data from said noise over an extended range of delay error at an extended-range bank of correlators, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal;

estimating the radio navigation data signal power and noise power from the correlator output functions; and

updating the navigation state using a set of correlator moments, the calculation being based on the estimated radio navigation data signal power and noise power, and the correlator output functions; and

calculating said correlator moments, which represent statistical expectations of nonlinear functions of line-of-sight position errors, with a nonlinear filter, said correlator moments being calculated according to the equation;

$\begin{array}{c}{M}_{c,k}^{\left(s\right)}={\int <br><br>}_{-\mathrm{\infty <br><br>}}^{\mathrm{\infty <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{\tau <br><br>}<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^s<br><br>R_c^2<br><br>\left(\mathrm{\tau <br><br>}\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)<br><br><br><br>\mathrm{where}\\ \mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)=k<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}+\mathrm{\tau <br><br>}<br><br><br><br>\mathrm{and}\\ P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)=\frac{{\mathrm{<br><br>}}^{-\frac{{\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^2}{2<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}}{\sqrt{2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}.\end{array}$

82. A method for determining a multidimensional navigation state for a navigation platform based on radio navigation data transmitted by external terminals comprising:

receiving and amplifying said radio navigation data embedded in noise at a radio navigation data receiver;

extracting said radio navigation data from said noise over an extended range of delay error at an extended-range bank of correlators, said correlators processing said radio navigation data and said noise to generate correlator output functions indicative of degree of correlation for each correlator in the extended-range bank between said radio navigation data and a known internal replica signal;

estimating the radio navigation data signal power and noise power from the correlator output functions; and

updating the navigation state using a set of correlator moments, the calculation being based on the estimated radio navigation data signal power and noise power, and the correlator output functions; and

calculating said correlator moments, which represent statistical expectations of nonlinear functions of line-of-sight position errors, said correlator moments being calculated according to the equation;

$\begin{array}{c}{M}_{c,k}^{\left(s\right)}={\int <br><br>}_{-\mathrm{\infty <br><br>}}^{\mathrm{\infty <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{\tau <br><br>}<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^s<br><br>R_c^2<br><br>\left(\mathrm{\tau <br><br>}\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)<br><br><br><br>\mathrm{where}\\ \mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)=k<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\Delta <br><br>}+\mathrm{\tau <br><br>}<br><br><br><br>\mathrm{and}\\ P_{\mathrm{\alpha <br><br>}}<br><br>\left(\mathrm{\tau <br><br>},k,j\right)=\frac{{\mathrm{<br><br>}}^{-\frac{{\left(\mathrm{\alpha <br><br>}<br><br>\left(\mathrm{\tau <br><br>},k\right)\right)}^2}{2<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}}{\sqrt{2<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\pi <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\mathrm{los}}<br><br>\left(j\right)}}.\end{array}$