Methods for generating accuracy information on an ionosphere model for satellite navigation applications

• US 9,557,419 B2
• Filed: 12/09/2013
• Issued: 01/31/2017
• Est. Priority Date: 12/18/2012
• Status: Active Grant

• Current Assignee: Trimble Inc.
• Original Assignee: Trimble Inc.
• Inventors: Drescher, Ralf, Chen, Xiaoming
• Primary Examiner(s): Phan, Dao
• Application Number:
  US14/101,119
• Publication Number:
  US 20140292573A1
• Time in Patent Office: 1,149 Days
• Field of Search: 342/357.21, 342/357.23, 342/357.44, 342/357.46, 342/357.51, 701/469, 701/484
• US Class Current: 1/1
• CPC Class Codes: G01S 19/074 providing integrity data, e.g. WAAS
• CPC Class Codes: G01S 19/13 Receivers

Abstract

Accuracy information for an ionosphere model is generated. Phase residual information of a parameter estimation procedure is obtained and coordinates of pierce points are computed on a sphere around the earth. The coordinates indicate where signals pierce the sphere. Phase residual information is mapped for each pierce point. A grid of equidistant points is put on the sphere and the pierce points are identified. For each selected grid point, vertical accuracy information is computed based on vertical residual information, and a scale factor is computed based on the vertical accuracy information. The accuracy information for the ionosphere model is generated based on the vertical accuracy information computed for the selected grid points and an overall scale factor computed based on computed scale factors.

Claims

1. Method, carried out by a computer or set of computers, for generating accuracy information for an ionosphere model calculated using a parameter estimation procedure based on phase observations made using a plurality of ground stations, wherein

each phase observation is obtained from at least two signals transmitted from a satellite to a ground station, each of the at least two signals having a different frequency, and

the accuracy information is usable in a GNSS positioning system for estimating a position of a rover,

the method comprising the steps of:

for each of at least some of the phase observations having fixed or converged ambiguities,

obtaining phase residual information of the parameter estimation procedure; and

computing coordinates of a pierce point on a sphere around the earth, the sphere being defined by an ionosphere single layer model, wherein the coordinates indicate where the at least two signals from which the phase observation has been obtained pierce the sphere;

mapping, for each pierce point, the phase residual information of the corresponding phase observation to the vertical at the pierce point to generate vertical residual information;

for each of at least some of the points of an equidistant grid on the sphere, each point being hereinafter referred to as “

grid point”

, identifying pierce points within a threshold distance from the grid point on the sphere;

selecting grid points for which the number of identified pierce points exceeds a threshold number;

computing, for each selected grid point,

vertical accuracy information at the selected grid point based on the vertical residual information of the identified pierce points, and

a scale factor based on the vertical accuracy information computed for the selected grid point; and

generating the accuracy information for the ionosphere model based on

the vertical accuracy information computed for the selected grid points, and

an overall scale factor computed based on the computed scale factors.

2. Method of claim 1, wherein the ionosphere model provides a slant total electron content along a signal path through the ionosphere.

3. Method of claim 1, wherein the ionosphere single layer model concentrates the phase residual information of the parameter estimation procedure mapped to a vertical on the sphere.

4. Method of claim 1, wherein each phase observation is obtained from two of the at least two signals using a geometric-free linear combination.

5. Method of claim 1, wherein the vertical accuracy information is a weighted root mean square computed from vertical residual information of the identified pierce points.

6. Method of claim 1, wherein generating the accuracy information comprises:

computing, for each of all grid points of the equidistant grid, accuracy information based on the vertical accuracy information and the overall scale factor;

computing coefficients of a functional expansion using a parameter estimation based on the accuracy information of each grid point to generate accuracy information covering the sphere.

7. Method of claim 6, wherein the step of computing accuracy information for each of all grid points of the equidistant grid comprises:

if the number of identified pierce points for the grid point is smaller than a first threshold number, computing the accuracy information by scaling a standard deviation of the ionosphere model at the grid point using at least the overall scale factor,

if the number of identified pierce points for the grid point is larger than a second threshold number, using the vertical accuracy information of the grid point as the accuracy information, and

if the number of identified pierce points for the grid point is larger than or equal to the first threshold number and smaller than or equal to the second threshold number, computing the accuracy information by using a transition function between the vertical accuracy information of the grid point and the scaled standard deviation of the ionosphere model at the grid point,

wherein the first threshold number is smaller than the second threshold number.

8. Method of claim 7, wherein the accuracy information is computed using the following formula:

AI=w(n)·

s·

wScale·

factor(n)+(1−

w(n))·

wRMS,

wherein

AI is the accuracy information,

s is the standard deviation of the ionosphere model at the grid point,

wScale is the overall scale factor,

wRMS is the vertical accuracy information of the grid point,

n denotes the number of identified pierce points for the grid point,

n1 denotes the first threshold number,

n2 denotes the second threshold number,

w(n)=1 if n<

n1,

w(n)=0 if n>

n2,

0<

w(n)<

1 if n1≦

n≦

n2,

1≦

factor(n)≦

3 if n≦

n2.

9. Method of claim 7, wherein the accuracy information is computed using the following formula:

AI=w(n)·

s·

wScale·

factor(n)+(1−

w(n))·

wRMS,

wherein

AI is the accuracy information,

s is the standard deviation of the ionosphere model at the grid point,

wScale is the overall scale factor,

wRMS is the vertical accuracy information of the grid point,

n denotes the number of identified pierce points for the grid point,

n1 denotes the first threshold number,

n2 denotes the second threshold number,

w(n)=1 if n<

n1,

w(n)=0 if n>

n2,

0<

w(n)<

1 if n1≦

n≦

n2,

1≦

factor(n)≦

1.5 if n≦

n2.

10. A non-transitory computer readable medium having a computer program stored thereon, the computer program comprising computer-executable instructions configured, when executed on a computer, to cause the computer to carry out the method according to claim 1.

11. Processing entity, for generating accuracy information for an ionosphere model calculated using a parameter estimation procedure based on phase observations made using a plurality of ground stations, wherein

each phase observation is obtained from at least two signals transmitted from a satellite to a ground station, each of the at least two signals having a different frequency, and

the accuracy information is usable in a GNSS positioning system for estimating a position of a rover,

the processing entity comprising:

an obtaining unit configured to, for each of at least some of the phase observations having fixed or converged ambiguities,

obtain phase residual information of the parameter estimation procedure; and

compute coordinates of a pierce point on a sphere around the earth, the sphere being defined by an ionosphere single layer model, wherein the coordinates indicate where the at least two signals from which the phase observation has been obtained pierce the sphere;

a mapping unit configured to map, for each pierce point, the phase residual information of the corresponding phase observation to the vertical at the pierce point to generate vertical residual information;

an identifying unit configured to, for each of at least some of the points of an equidistant grid on the sphere, each point being hereinafter referred to as “

grid point”

, identify pierce points within a threshold distance from the grid point on the sphere;

a selecting unit configured to select grid points for which the number of identified pierce points exceeds a threshold number;

a computing unit configured to compute, for each selected grid point, vertical accuracy information at the selected grid point based on the

vertical residual information of the identified pierce points, and a scale factor based on the vertical accuracy information computed for the selected grid point; and

a generating unit configured to generate the accuracy information for the ionosphere model based on the vertical accuracy information computed for the selected grid points and an overall scale factor computed based on the computed scale factors.

12. Processing entity of claim 11, wherein the ionosphere model provides a slant total electron content along a signal path through the ionosphere.

13. Processing entity of claim 11, wherein the ionosphere single layer model concentrates the phase residual information of the parameter estimation procedure mapped to a vertical on the sphere.

14. Processing entity of claim 11, wherein each phase observation is obtained from two of the at least two signals using a geometric-free linear combination.

15. Processing entity of claim 11, wherein the vertical accuracy information is a weighted root mean square computed from vertical residual information of the identified pierce points.

16. Processing entity of claim 11, wherein the overall scale factor is a weighted mean of the scale factors.

17. Processing entity of claim 11, wherein the generating unit is further configured to:

compute, for each of all grid points of the equidistant grid, accuracy information based on the vertical accuracy information and the overall scale factor; and

compute coefficients of a functional expansion using a parameter estimation based on the accuracy information of each grid point to generate accuracy information covering the sphere.

18. Processing entity of claim 17, wherein generating unit is further configured to:

if the number of identified pierce points for the grid point is smaller than a first threshold number, compute the accuracy information by scaling the standard deviation of the ionosphere model at the grid point using at least the overall scale factor,

if the number of identified pierce points for the grid point is larger than a second threshold number, use the vertical accuracy information of the grid point as the accuracy information, and

if the number of identified pierce points for the grid point is larger than or equal to the first threshold number and smaller than or equal to the second threshold number, compute the accuracy information by using a transition function between the vertical accuracy information of the grid point and the scaled standard deviation of the ionosphere model at the grid point,

wherein the first threshold number is smaller than the second threshold number.

19. Processing entity of claim 18, wherein the generating unit is further configured to compute the accuracy information using the following formula:

AI=w(n)·

s·

wScale·

factor(n)+(1−

w(n))·

wRMS,

wherein

AI is the accuracy information,

s is the standard deviation of the ionosphere model at the grid point,

wScale is the overall scale factor,

wRMS is the vertical accuracy information of the grid point,

n denotes the number of identified pierce points for the grid point,

n1 denotes the first threshold number,

n2 denotes the second threshold number,

w(n)=1 if n<

n1,

w(n)=0 if n>

n2,

0<

w(n)<

1 if n1≦

n≦

n2,

1≦

factor(n)≦

3 if n≦

n2.

20. Processing entity of claim 18, wherein the generating unit is further configured to compute the accuracy information using the following formula:

AI=w(n)·

s·

wScale·

factor(n)+(1−

w(n))·

wRMS,

wherein

AI is the accuracy information,

s is the standard deviation of the ionosphere model at the grid point,

wScale is the overall scale factor,

wRMS is the vertical accuracy information of the grid point,

n denotes the number of identified pierce points for the grid point,

n1 denotes the first threshold number,

n2 denotes the second threshold number,

w(n)=1 if n<

n1,

w(n)=0 if n>

n2,

0<

w(n)<

1 if n1≦

n≦

n2,

1≦

factor(n)≦

1.5 if n≦

n2.