Granted Patent B2

US 9,591,438 · App. 14/138,412 · Granted Mar 7, 2017

Inventors: Arun Raghupathy (Bangalore, IN); Ganesh Pattabiraman (Los Gatos, CA); Subramanian S Meiyappan (San Jose, CA); Hari Sankar (Santa Clara, CA)

Assignee: NextNav, LLC

H04W4/02G01S1/08G01S5/10G01S19/11G01S19/24G01S19/42G01S19/46H04B7/2618H04W72/005H04W72/0446

Abstract

Claims (112)

1. A method for estimating one or more positions of a receiver, wherein the method comprises:

generating, for each of a plurality of positioning signals transmitted from a plurality of terrestrial transmitters and received by the receiver, a cross-correlation function by cross-correlating one or more signal samples extracted from that positioning signal with a reference sequence corresponding to that positioning signal;

determining a vector of cross-correlation samples from each cross-correlation function;

identifying, for each of the positioning signals, a time of arrival estimate corresponding to an earliest arriving signal path of one or more signal paths corresponding to that positioning signal using a high resolution time of arrival measurement method; and

estimating a first position of the receiver based on the time of arrival estimate.

2. The method of claim 1 , wherein the time of arrival estimate identified for each of the positioning signals is identified by applying the high resolution time of arrival measurement method to the vector of cross-correlation samples corresponding to that positioning signal.

3. The method of claim 1 , wherein each vector of cross-correlation samples includes the peak of the cross-correlation function.

4. The method of claim 1 , wherein each vector of cross-correlation samples includes a first set of cross-correlation samples left of a peak of the cross-correlation function, and a second set of cross-correlation samples right of the peak of the cross-correlation function.

5. The method of claim 1 , wherein each reference sequence is a pseudorandom sequence.

6. The method of claim 1 , wherein the high resolution time of arrival measurement method is based on at least one of a MUSIC algorithm, an ESPRIT algorithm, or an Eigen-space decomposition method.

7. The method of claim 1 , wherein the method comprises:

generating a reference vector from a correlation function determined by a calculated function or a measurement in a channel environment that has low noise and separable or no multipath components.

8. The method of claim 1 , wherein the method comprises:

improving a signal-to-noise ratio in the vector by coherently averaging across at least one of a plurality of pseudorandom code frames and a plurality of bits.

9. The method of claim 1 , wherein the method comprises:

calculating a Fourier Transform using the vector.

10. The method of claim 1 , wherein the method comprises:

generating a frequency domain estimate of a channel;

generating a reduced channel estimate vector from the frequency domain estimate of the channel;

defining an estimated covariance matrix of the reduced channel estimate vector; and

performing singular value decomposition on the estimated covariance matrix.

11. The method of claim 1 , wherein the method comprises:

generating a vector of sorted singular values; and

using the vector of sorted singular values to separate signal and noise subspaces.

12. The method of claim 1 , wherein the method comprises:

generating a noise subspace matrix.

13. The method of claim 1 , wherein the method comprises:

determining the first position of the receiver based on a non-linear objective function and a best estimate of the first position as a set of position parameters that minimize the objective function.

14. The method of claim 1 , wherein the method comprises:

determining the first position of the receiver based on a solution to a set of linearized equations using a least squares method.

15. The method of claim 1 , wherein the high resolution time of arrival measurement method is based on at least one of a signal space separation method, a noise space separation method, a singular value decomposition method, or a covariance estimation method.

16. The method of claim 1 , wherein the method comprises:

selecting a first set of cross-correlation samples left of a peak of the cross-correlation function and a second set of cross-correlation samples right of the peak of the cross-correlation function; and

selecting uncorrelated noise samples to obtain information regarding a noise sub-space.

17. The method of claim 1 , wherein the time of arrival estimate corresponding to the earliest arriving signal path is identified by:

generating a reference vector from a correlation function determined by a calculated function or a measurement in a channel environment that has low noise and separable or no multipath components;

improving a signal-to-noise ratio in the vector by coherently averaging across at least one of a plurality of pseudorandom code frames and a plurality of bits;

calculating a Fourier Transform of the vector;

generating a frequency domain estimate of a channel using the Fourier Transform of the vector and a Fourier Transform of the reference vector;

generating a reduced channel estimate vector from the frequency domain estimate of the channel;

defining an estimated covariance matrix of the reduced channel estimate vector;

performing singular value decomposition on the estimated covariance matrix;

generating a vector of sorted singular values;

using the vector of sorted singular values to separate signal and noise subspaces;

generating a noise subspace matrix; and

using the noise subspace matrix to identify the time of arrival estimate corresponding to the earliest arriving signal path.

18. One or more non-transitory computer-readable media embodying program instructions that, when executed by one or more processors, cause the one or more processors to implement a method for estimating one or more positions of a receiver, wherein the method comprises:

generating, for each of a plurality of positioning signals transmitted from a plurality of terrestrial transmitters and received by the receiver, a cross-correlation function by cross-correlating one or more signal samples extracted from that positioning signal with a reference sequence corresponding to that positioning signal;

determining a vector of cross-correlation samples from each cross-correlation function;

identifying, for each of the positioning signals, a time of arrival estimate corresponding to an earliest arriving signal path of one or more signal paths corresponding to that positioning signal using a high resolution time of arrival measurement method; and

estimating a first position of the receiver based on the time of arrival estimate.

19. The one or more non-transitory computer-readable media of claim 18 , wherein the time of arrival estimate identified for each of the positioning signals is identified by applying the high resolution time of arrival measurement method to the vector of cross-correlation samples corresponding to that positioning signal.

20. The one or more non-transitory computer-readable media of claim 18 , wherein each vector of cross-correlation samples includes the peak of the cross-correlation function.

21. The one or more non-transitory computer-readable media of claim 18 , wherein each vector of cross-correlation samples includes a first set of cross-correlation samples left of a peak of the cross-correlation function, and a second set of cross-correlation samples right of the peak of the cross-correlation function.

22. The one or more non-transitory computer-readable media of claim 18 , wherein each reference sequence is a pseudorandom sequence.

23. The one or more non-transitory computer-readable media of claim 18 , wherein the high resolution time of arrival measurement method is based on at least one of a MUSIC algorithm, an ESPRIT algorithm, or an Eigen-space decomposition method.

24. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

generating a reference vector from a correlation function determined by a calculated function or a measurement in a channel environment that has low noise and separable or no multipath components.

25. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

improving a signal-to-noise ratio in the vector by coherently averaging across at least one of a plurality of pseudorandom code frames and a plurality of bits.

26. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

calculating a Fourier Transform using the vector.

27. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

generating a frequency domain estimate of a channel;

generating a reduced channel estimate vector from the frequency domain estimate of the channel;

defining an estimated covariance matrix of the reduced channel estimate vector; and

performing singular value decomposition on the estimated covariance matrix.

28. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

generating a vector of sorted singular values; and

using the vector of sorted singular values to separate signal and noise subspaces.

29. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

generating a noise subspace matrix.

30. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

determining the first position of the receiver based on a non-linear objective function and a best estimate of the first position as a set of position parameters that minimize the objective function.

31. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

determining the first position of the receiver based on a solution to a set of linearized equations using a least squares method.

32. The one or more non-transitory computer-readable media of claim 18 , wherein the high resolution time of arrival measurement method is based on at least one of a signal space separation method, a noise space separation method, a singular value decomposition method, or a covariance estimation method.

33. The one or more non-transitory computer-readable media of claim 18 , wherein the method comprises:

selecting a first set of cross-correlation samples left of a peak of the cross-correlation function and a second set of cross-correlation samples right of the peak of the cross-correlation function; and

selecting uncorrelated noise samples to obtain information regarding a noise sub-space.

34. The one or more non-transitory computer-readable media of claim 18 , wherein the time of arrival estimate corresponding to the earliest arriving signal path is identified by:

generating a reference vector from a correlation function determined by a calculated function or a measurement in a channel environment that has low noise and separable or no multipath components;

improving a signal-to-noise ratio in the vector by coherently averaging across at least one of a plurality of pseudorandom code frames and a plurality of bits;

calculating a Fourier Transform of the vector;

generating a frequency domain estimate of a channel using the Fourier Transform of the vector and a Fourier Transform of the reference vector;

generating a reduced channel estimate vector from the frequency domain estimate of the channel;

defining an estimated covariance matrix of the reduced channel estimate vector;

performing singular value decomposition on the estimated covariance matrix;

generating a vector of sorted singular values;

using the vector of sorted singular values to separate signal and noise subspaces;

generating a noise subspace matrix; and

using the noise subspace matrix to identify the time of arrival estimate corresponding to the earliest arriving signal path.

35. A system for estimating one or more positions of a receiver, wherein the system comprises:

means for generating, for each of a plurality of positioning signals transmitted from a plurality of terrestrial transmitters and received by the receiver, a cross-correlation function by cross-correlating one or more signal samples extracted from that positioning signal with a reference sequence corresponding to that positioning signal;

means for determining a vector of cross-correlation samples from each cross-correlation function;

means for identifying, for each of the positioning signals, a time of arrival estimate corresponding to an earliest arriving signal path of one or more signal paths corresponding to that positioning signal using a high resolution time of arrival measurement method; and

means for estimating a first position of the receiver based on the time of arrival estimate.

36. The system of claims 35 , wherein the system comprises:

means for selecting a first set of cross-correlation samples left of a peak of the cross-correlation function and a second set of cross-correlation samples right of the peak of the cross-correlation function; and

means for selecting uncorrelated noise samples to obtain information regarding a noise sub-space.

37. The system of claims 35 , wherein the system comprises:

means for generating a reference vector from a correlation function determined by a calculated function or a measurement in a channel environment that has low noise and separable or no multipath components;

means for improving a signal-to-noise ratio in the vector by coherently averaging across at least one of a plurality of pseudorandom code frames and a plurality of bits;

means for calculating a Fourier Transform of the vector;

means for generating a frequency domain estimate of a channel using the Fourier Transform of the vector and a Fourier Transform of the reference vector;

means for generating a reduced channel estimate vector from the frequency domain estimate of the channel;

means for defining an estimated covariance matrix of the reduced channel estimate vector;

means for performing singular value decomposition on the estimated covariance matrix;

means for generating a vector of sorted singular values;

means for using the vector of sorted singular values to separate signal and noise subspaces;

means for generating a noise subspace matrix; and

means for using the noise subspace matrix to identify the time of arrival estimate corresponding to the earliest arriving signal path.

Assignments (4)

Continuity (12)