Systems and methods for estimating a position of a receiver

• US 10,254,379 B2
• Filed: 01/22/2018
• Issued: 04/09/2019
• Est. Priority Date: 02/08/2017
• Status: Active Grant

• Current Assignee: NextNav, LLC
• Original Assignee: NextNav, LLC
• Inventors: Sendonaris, Andrew, Deshpande, Sameet, Mahmood, Mir Hamza
• Primary Examiner(s): Ngo, Chuong A
• Application Number:
  US15/877,096
• Publication Number:
  US 20180224519A1
• Time in Patent Office: 442 Days
• CPC Class Codes: G01S 19/20 Integrity monitoring, fault detection or fault isolation of space segment
• CPC Class Codes: G01S 19/24 Acquisition or tracking or demodulation of signals transmitted by the system synchronisation aspects of direct sequence spread spectrum modulation H04B1/7073
• CPC Class Codes: G01S 19/31 Acquisition or tracking of other signals for positioning
• CPC Class Codes: G01S 5/012 Identifying whether indoors or outdoors
• CPC Class Codes: G01S 5/0263 by combining or switching between positions derived from two or more separate positioning systems
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
• CPC Class Codes: G01S 5/14 Determining absolute distances from a plurality of spaced points of known location
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: H04W 4/02 Services making use of location information

Abstract

Estimating a position of a mobile device. Particular systems and methods for estimating a position of a mobile device using information from two positioning technologies determine different position estimates for the mobile device using different positioning technologies, and determine a final position estimate for the mobile device using a weighted combination of the different position estimates. In some implementations, the weighted combination is a weighted average or a weighted median of the different position estimates. Weights may be determined using respective uncertainty metrics corresponding to the respective position estimates.

Claims

1. A method for estimating a position of a mobile device using information from two positioning technologies wherein the method comprises:

determining a first position estimate for the mobile device using a first positioning technology;

determining a second position estimate for the mobile device using a second positioning technology;

determining a first uncertainty metric that represents uncertainty of the first position estimate;

determining a second uncertainty metric that represents uncertainty of the second position estimate;

determining a first variance using the first uncertainty metric;

determining a second variance using the second uncertainty metric;

determining a total variance using the first variance and the second variance;

determining a first ratio of the first variance to the total variance;

determining a second ratio of the second variance to the total variance;

determining a first weight using the first ratio;

determining a second weight using the second ratio;

applying the first weight to the first position estimate to generate a first weighted position estimate;

applying the second weight to the second position estimate to generate a second weighted position estimate, and

determining a final position estimate for the mobile device using an average of at least the first weighted position estimate and the second weighted position estimate.

2. The method of claim 1, wherein the total variance comprises a sum of a plurality of variances that include the first variance and the second variance.

3. A method for estimating a position of a mobile device using information from two positioning technologies, wherein the method comprises:

determining a first position estimate for the mobile device using a first positioning technology;

determining a second position estimate for the mobile device using a second positioning technology;

determining a first uncertainty metric that represents uncertainty of the first position estimate;

determining a second uncertainty metric that represents uncertainty of the second position estimate;

determining a first corrected uncertainty metric by inputting the first uncertainty metric into a first mapping function;

determining a second corrected uncertainty metric by inputting the second uncertainty metric into a second mapping function;

determining a first weight using the first corrected uncertainty metric;

determining a second weight using the second corrected uncertainty metric;

applying the first weight to the first position estimate to generate a first weighted position estimate; and

applying the second weight to the second position estimate to generate a second weighted position estimate;

determining a final position estimate for the mobile device using a combination of the first weighted position estimate and the second weighted position estimate.

4. The method of claim 3, wherein:

the first mapping function multiplies the first uncertainty metric by a first multiplied coefficient to produce a first product, and sums the first product and a first added coefficient to produce the first corrected uncertainty metric; and

the second mapping function multiplies the second uncertainty metric by a second multiplied coefficient to produce a second product, and sums the second product and a second added coefficient to produce the second corrected uncertainty metric.

5. A method for estimating a position of a mobile device using information from two positioning technologies, wherein the method comprises:

determining a first position estimate for the mobile device using a first positioning technology; and

using a second positioning technology to;

initialize a hypothesis grid, where each hypothesis in the grid has an assumed location and a weight;

for each hypothesis in the grid, recalculate the weight of that hypothesis based on a respective distance between the assumed location and the first position estimate; and

normalizing the recalculated weights such that the sum of all of the recalculated weights is equal to one;

determine a final position estimate for the mobile device using the normalized weights.

6. The method of claim 5, wherein the final position estimate is determined using the normalized weights by using the normalized weights as a confidence factor or measurement noise estimate in a Kalman or particle filter.

7. The method of claim 5, wherein for each particular hypothesis in the grid, the recalculated weight of the particular hypothesis is determined using (a) the weight of the particular hypothesis and (b) a multiplier that is determined using an exponential function of a negative value, wherein the value is determined by dividing (i) a square of the respective distance between the assumed location of the particular hypothesis and the first position estimate by (ii) a variance of the first position estimate.

8. The method of claim 5, wherein for each particular hypothesis in the grid, the recalculated weight of the particular hypothesis is determined using (a) the weight of the particular hypothesis and (b) a multiplier that is (i) larger than each other multiplier used to determine a respective recalculated weight of a respective hypothesis in the grid that has a respective assumed location that is further away from the first position estimate than the assumed location of the particular hypothesis, and (ii) smaller than each other multiplier used to determine a respective recalculated weight of a respective hypothesis in the grid that has a respective assumed location that is closer to the first position estimate than the assumed location of the particular hypothesis.

9. A method for estimating a position of a mobile device using information from two positioning technologies, wherein the method comprises:

for each transmitter in a transmitter network of a first positioning technology, estimating a pseudorange between an unknown location of the mobile device and a known location of that transmitter;

determining an estimated position of the unknown location of the mobile device using a second positioning technology;

determining an uncertainty metric of the estimated position;

for each transmitter in the transmitter network, determining a distance between the estimated position and the known location of that transmitter;

for each transmitter in the transmitter network, determining a pseudorange error as the difference between the estimated pseudorange for that transmitter and the determined distance for that transmitter;

determining a scale factor that is based on the uncertainty metric;

for each transmitter in the transmitter network, determining a pseudorange correction for that transmitter by scaling the determined pseudorange error for that transmitter using the scale factor; and

for each transmitter in the transmitter network, determining a corrected pseudorange for that transmitter as the difference between the estimated pseudorange for that transmitter and the pseudorange correction for that transmitter.

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

determining a final position estimate for the mobile device using the corrected pseudoranges.

11. One or more non-transitory machine-readable media embodying program instructions that, when executed by one or more machines, cause the one or more machines to implement a method for estimating a position of a mobile device using information from two positioning technologies wherein the method comprises:

determining a first position estimate for the mobile device using a first positioning technology;

determining a second position estimate for the mobile device using a second positioning technology;

determining a first uncertainty metric that represents uncertainty of the first position estimate;

determining a second uncertainty metric that represents uncertainty of the second position estimate;

determining a first variance using the first uncertainty metric;

determining a second variance using the second uncertainty metric;

determining a total variance using the first variance and the second variance;

determining a first ratio of the first variance to the total variance;

determining a second ratio of the second variance to the total variance;

determining a first weight using the first ratio;

determining a second weight using the second ratio;

applying the first weight to the first position estimate to generate a first weighted position estimate;

applying the second weight to the second position estimate to generate a second weighted position estimate, and

determining a final position estimate for the mobile device using an average of at least the first weighted position estimate and the second weighted position estimate.

12. The one or more non-transitory machine-readable media of claim 11, wherein the total variance comprises a sum of a plurality of variances that include the first variance and the second variance.

13. One or more non-transitory machine-readable media embodying program instructions that, when executed by one or more machines, cause the one or more machines to implement a method for estimating a position of a mobile device using information from two positioning technologies, wherein the method comprises:

determining a first position estimate for the mobile device using a first positioning technology;

determining a second position estimate for the mobile device using a second positioning technology;

determining a first uncertainty metric that represents uncertainty of the first position estimate;

determining a second uncertainty metric that represents uncertainty of the second position estimate;

determining a first corrected uncertainty metric by inputting the first uncertainty metric into a first mapping function;

determining a second corrected uncertainty metric by inputting the second uncertainty metric into a second mapping function;

determining a first weight using the first corrected uncertainty metric;

determining a second weight using the second corrected uncertainty metric;

applying the first weight to the first position estimate to generate a first weighted position estimate; and

applying the second weight to the second position estimate to generate a second weighted position estimate;

determining a final position estimate for the mobile device using a combination of the first weighted position estimate and the second weighted position estimate.

14. The one or more non-transitory machine-readable media of claim 13, wherein:

the first mapping function multiplies the first uncertainty metric by a first multiplied coefficient to produce a first product, and sums the first product and a first added coefficient to produce the first corrected uncertainty metric; and

the second mapping function multiplies the second uncertainty metric by a second multiplied coefficient to produce a second product, and sums the second product and a second added coefficient to produce the second corrected uncertainty metric.

15. One or more non-transitory machine-readable media embodying program instructions that, when executed by one or more machines, cause the one or more machines to implement a method for estimating a position of a mobile device using information from two positioning technologies, wherein the method comprises:

determining a first position estimate for the mobile device using a first positioning technology; and

using a second positioning technology to;

initialize a hypothesis grid, where each hypothesis in the grid has an assumed location and a weight;

for each hypothesis in the grid, recalculate the weight of that hypothesis based on a respective distance between the assumed location and the first position estimate; and

normalizing the recalculated weights such that the sum of all of the recalculated weights is equal to one;

determine a final position estimate for the mobile device using the normalized weights.

16. The one or more non-transitory machine-readable media of claim 15, wherein the final position estimate is determined using the normalized weights by using the normalized weights as a confidence factor or measurement noise estimate in a Kalman or particle filter.

17. The one or more non-transitory machine-readable media of claim 15, wherein for each particular hypothesis in the grid, the recalculated weight of the particular hypothesis is determined using (a) the weight of the particular hypothesis and (b) a multiplier that is determined using an exponential function of a negative value, wherein the value is determined by dividing (i) a square of the respective distance between the assumed location of the particular hypothesis and the first position estimate by (ii) a variance of the first position estimate.

18. The one or more non-transitory machine-readable media of claim 15, wherein for each particular hypothesis in the grid, the recalculated weight of the particular hypothesis is determined using (a) the weight of the particular hypothesis and (b) a multiplier that is (i) larger than each other multiplier used to determine a respective recalculated weight of a respective hypothesis in the grid that has a respective assumed location that is further away from the first position estimate than the assumed location of the particular hypothesis, and (ii) smaller than each other multiplier used to determine a respective recalculated weight of a respective hypothesis in the grid that has a respective assumed location that is closer to the first position estimate than the assumed location of the particular hypothesis.

19. One or more non-transitory machine-readable media embodying program instructions that, when executed by one or more machines, cause the one or more machines to implement a method for estimating a position of a mobile device using information from two positioning technologies, wherein the method comprises:

for each transmitter in a transmitter network of a first positioning technology, estimating a pseudorange between an unknown location of the mobile device and a known location of that transmitter;

determining an estimated position of the unknown location of the mobile device using a second positioning technology;

determining an uncertainty metric of the estimated position;

for each transmitter in the transmitter network, determining a distance between the estimated position and the known location of that transmitter;

for each transmitter in the transmitter network, determining a pseudorange error as the difference between the estimated pseudorange for that transmitter and the determined distance for that transmitter;

determining a scale factor that is based on the uncertainty metric;

for each transmitter in the transmitter network, determining a pseudorange correction for that transmitter by scaling the determined pseudorange error for that transmitter using the scale factor; and

for each transmitter in the transmitter network, determining a corrected pseudorange for that transmitter as the difference between the estimated pseudorange for that transmitter and the pseudorange correction for that transmitter.

20. The one or more non-transitory machine-readable media of claim 19, wherein the method comprises:

determining a final position estimate for the mobile device using the corrected pseudoranges.