Location determination system for vehicles

• US 20090138199A1
• Filed: 11/27/2007
• Published: 05/28/2009
• Est. Priority Date: 11/27/2007
• Status: Active Grant

• Current Assignee: Westinghouse Air Brake Technologies Corporation
• Original Assignee: General Electric Company
• Inventors: Bonanni, Pierino Gianni, Vachhani, Pramod, Mathews, Harry Kirk, Chan, David So Keung
• Granted Patent: US 9,606,240 B2
• Time in Patent Office: Days
• US Class Current: 701/214
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
• CPC Class Codes: G01S 19/50 whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks

Abstract

A vehicle location determination system and method provide an estimate of real time location of the vehicle along a route in response solely to vehicle GPS information and vehicle speed information such that the estimated real time distance is robust to errors and disturbances associated with both the vehicle GPS information and vehicle speed information to ensure the estimated real time location information is accurate.

Claims

1. A vehicle location determination system (LDS) configured to estimate real time location of the vehicle along a route in response solely to GPS information and vehicle speed information such that the estimated real time distance is robust to cumulative errors and disturbances associated with both the GPS information and vehicle speed information to ensure the estimated real time location information is accurate.

2. The vehicle LDS of claim 1, wherein the estimated real time location is generated via a Kalman filter configured to reject GPS data or vehicle speed data that exceeds respective threshold limits.

3. The vehicle LDS of claim 1, wherein the estimated real time location is generated via a Kalman filter configured to integrate vehicle speed data in response to speed bias data generated in response to the GPS information.

4. The vehicle LDS of claim 3, wherein the Kalman filter is further configured to intermittently reset an estimator and provide a corrected reference value of speed bias based on a critical milepost.

5. The vehicle LDS of claim 4, wherein the Kalman filter is further configured to process the most recent GPS information via projection of GPS values onto a database comprising the critical milepost.

6. The vehicle LDS of claim 3, wherein the Kalman filter is further configured to intermittently reset an estimator and provide a corrected reference value of GPS information based on a regular milepost.

7. The vehicle LDS of claim 1, wherein the estimated real time location is generated via a Kalman filter configured to process the most recent GPS information via projection of GPS values onto a database.

8. The vehicle LDS of claim 7, wherein the database comprises known latitude data, longitude data, and cumulative distance data associated with the route.

9. The vehicle LDS of claim 7, wherein the projection is a great arc projection.

10. The vehicle LDS of claim 9, wherein the Kalman filter is further configured to generate a quality indicator signal associated with the estimated real time location information in response to GPS projection data and associated covariance data.

11. The vehicle LDS of claim 7, wherein the projection is a non-linear least squares projection.

12. The vehicle LDS of claim 1, wherein the estimated real time location is generated via a two-state variable Kalman filter, wherein first state variable comprises vehicle position along the route and the second state variable comprises vehicle speed bias.

13. The vehicle LDS of claim 1, wherein the estimated real time location is generated via a single-state variable Kalman filter, wherein the single-state variable comprises vehicle position along the route.

14. The vehicle LDS of claim 1, wherein the vehicle comprises a locomotive and the route comprises a locomotive track.

15. A method of determining real time location of a vehicle along a route, the method comprising:

measuring GPS information associated with the vehicle;

measuring speed information associated with the vehicle; and

estimating real time distance of the vehicle along the route solely in response to the GPS information and the vehicle speed information such that the estimated real time distance is robust to cumulative errors and disturbances associated with both the GPS information and vehicle speed information to ensure the estimated real time location information is accurate.

16. The method of claim 15, wherein estimating real time distance of the vehicle along the route comprises processing the GPS information and the vehicle speed information via a Kalman filter configured to reject GPS data or vehicle speed data that exceeds respective threshold limits.

17. The method of claim 15, wherein estimating real time distance of the vehicle along the route comprises processing the GPS information and the vehicle speed information via a Kalman filter configured to integrate the vehicle measured speed information in response to speed bias data generated in response to the GPS information.

18. The method of claim 17, wherein estimating real time distance of the vehicle along the route further comprises processing the GPS information and the vehicle speed information via the Kalman filter further configured to intermittently reset a real time distance estimator and provide a corrected reference value of the speed bias data based on a critical milepost.

19. The method of claim 18, wherein estimating real time distance of the vehicle along the route further comprises processing the GPS information and the vehicle speed information via the Kalman filter further configured to process the most recent GPS information via projection of GPS values onto a database comprising the critical milepost.

20. The method of claim 15, wherein estimating real time distance of the vehicle along the route further comprises processing the GPS information and the vehicle speed information via a Kalman filter configured to process the most recent GPS information via projection of GPS values onto a database.

21. The method of claim 20, wherein processing the GPS information and the vehicle speed information via a Kalman filter configured to process the most recent GPS information via projection of GPS values onto a database comprises processing the GPS information and the vehicle speed information via the Kalman filter further configured to process the most recent GPS information via projection of GPS values onto a database comprising known latitude data, longitude data, and cumulative distance data associated with the route.

22. The method of claim 20, wherein processing the GPS information and the vehicle speed information via a Kalman filter configured to process the most recent GPS information via projection of GPS values onto a database comprises processing the GPS information and the vehicle speed information via the Kalman filter further configured to process the most recent GPS information via a great arc projection of GPS values onto a database comprising known latitude data, longitude data, and cumulative distance data associated with the route.

23. The method of claim 20, wherein processing the GPS information and the vehicle speed information via a Kalman filter configured to process the most recent GPS information via projection of GPS values onto a database comprises processing the GPS information and the vehicle speed information via the Kalman filter further configured to process the most recent GPS information via a non-linear least squares projection of GPS values onto a database comprising known latitude data, longitude data, and cumulative distance data associated with the route.

24. The method of claim 15, wherein estimating real time distance of the vehicle along the route comprises processing the GPS information and the vehicle speed information via a Kalman filter configured to generate a quality indicator signal associated with the estimated real time location information in response to GPS projection data and associated GPS covariance data.

25. The vehicle LDS of claim 15, wherein estimating real time distance of the vehicle along the route comprises processing the GPS information and the vehicle speed information via a two-state variable Kalman filter, wherein first state variable comprises vehicle position along the route and the second state variable comprises vehicle speed bias.

26. The vehicle LDS of claim 15, wherein estimating real time distance of the vehicle along the route comprises processing the GPS information and the vehicle speed information via a single-state variable Kalman filter, wherein the single-state variable comprises vehicle position along the route.

27. The vehicle LDS of claim 15, wherein estimating real time distance of the vehicle along the route comprises estimating real time distance of a locomotive along a locomotive track.

28. A vehicle location determination system (LDS) comprising:

a Kalman filter comprising;

a GPS processing module;

a speed integrator module; and

an estimator module;

wherein the GPS processing module, speed integrator module, and estimator module are configured together to estimate real time distance of the vehicle along a route in response solely to GPS information and vehicle speed information such that the estimated real time distance is robust to errors and disturbances associated with both the GPS information and vehicle speed information to ensure an estimated real time location is accurate.

29. The LDS of claim 28, wherein the Kalman filter further comprises a database comprising:

longitude data;

latitude data; and

cumulative distance data, wherein the longitude data, latitude data, and cumulative distance data together define regular milepost and critical milepost estimation locations.

30. The LDS of claim 29, wherein the Kalman filter further comprises a reset module configured to reset the estimator module via an updated speed bias that is determined at each critical milepost.

31. The LDS of claim 29, wherein the GPS processing module is configured to configured to process the most recent GPS information via a great arc projection of GPS values onto the database.

32. The LDS of claim 31, wherein the GPS processing module is further configured to perform an unscented transformation (UT) for covariance calculations during GPS processing that is carried out only when a GPS value updates and the value is in range of the database.

33. The LDS of claim 29, wherein the GPS processing module is configured to process the most recent GPS information via a nonlinear least-squares projection of GPS values onto the database.