Vehicle determination system and method using a kalman filter and critical milepost data

Patent metadata

Field	Value
Publication number	US-9606240-B2
Application number	US-99810307-A
Country	US
Kind code	B2
Filing date	Nov 27, 2007
Priority date	Nov 27, 2007
Publication date	Mar 28, 2017
Grant date	Mar 28, 2017

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.

First claim

The invention claimed is:

1. A vehicle location determination system (LDS) comprising:
a computer or processor;
a speed bias based speed integration module;
a Kalman filter based estimator, wherein the computer or processor is programmed to estimate real time location of the vehicle along a route via the Kalman filter based estimator that estimates the real time location in response solely to both GPS information and vehicle speed information; and
a critical milepost module comprising a database of identifying entries that are distinct and independent from the GPS information and the vehicle speed information, wherein the computer or processor is further programmed to reset the speed bias based integration module based upon the identifying entries in the database such that the estimated real time location 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 computer or processor is further programmed to reject GPS data or vehicle speed data that exceeds respective threshold limits.

3. The vehicle LDS of claim 1 , wherein the computer or processor is further programmed to intermittently reset and provide a corrected reference value of speed bias based on the identifying entries in the critical milepost database.

4. The vehicle LDS of claim 3 , wherein the computer or processor is further programmed to process the most recent GPS information via projection of GPS values onto the critical milepost database.

5. The vehicle LDS of claim 4 , wherein the database comprises known latitude data, longitude data, and cumulative distance data associated with the route.

6. The vehicle LDS of claim 4 , wherein the projection is a great arc projection.

7. The vehicle LDS of claim 6 , wherein the computer or processor is further programmed to generate a quality indicator signal associated with the estimated real time location information in response to GPS projection data and associated covariance data.

8. The vehicle LDS of claim 4 , wherein the projection is a non-linear least squares projection.

9. The vehicle LDS of claim 1 , wherein the Kalman filter based estimator comprises 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 based upon the critical milepost database.

10. The vehicle LDS of claim 1 , wherein the Kalman filter based estimator comprises a single-state variable Kalman filter, wherein the single-state variable comprises vehicle position along the route.

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

12. A method of determining real time location of a vehicle along a route, the method comprising:
providing a location determination system (LDS) comprising a computer or processor, a GPS processing module, speed bias based speed integration module, and a Kalman filter based estimator;
programming the computer or processor to measure GPS information via the GPS processing module, and measuring the GPS information via the GPS processing module associated with the vehicle;
programming the computer or processor to measure speed information via the speed bias based speed integration module and measuring the speed information via the speed bias based speed integration module associated with the vehicle;
estimating real time distance of the vehicle along the route solely in response to both the GPS information and the vehicle speed information via the Kalman filter based estimator; and
intermittently communicating a corrected value of speed bias to the speed bias based speed integration module and resetting the speed bias based speed integration module in response to both the corrected value of speed bias and predetermined critical milepost data that is distinct and independent of 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.

13. The method of claim 12 , further comprising rejecting GPS data or vehicle speed data via the Kalman filter based estimator that exceeds respective threshold limits.

14. The method of claim 12 , wherein estimating real time distance of the vehicle along the route further comprises processing the most recent GPS information via projection of GPS values onto the database comprising the critical milepost entries and generating the corrected value of speed bias therefrom via the Kalman filter based estimator.

15. The method of claim 14 , wherein projection of GPS values onto a database comprising the critical milepost entries comprises projection of GPS values onto a database comprising known latitude data, longitude data, and cumulative distance data associated with the route.

16. The method of claim 14 , wherein projection of GPS values onto the critical milepost database comprises a great arc projection of GPS values onto a database comprising known latitude data, longitude data, and cumulative distance data associated with the route.

17. The method of claim 14 , wherein projection of GPS values onto the critical milepost database comprises 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.

18. The method of claim 12 , wherein estimating real time distance of the vehicle along the route comprises processing the GPS information and the vehicle speed information via the Kalman filter based estimator 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.

19. The method of claim 12 , 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 based estimator, wherein first state variable comprises vehicle position along the route and the second state variable comprises vehicle speed bias based upon the critical milepost database entries.

20. The method of claim 12 , 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 based estimator, wherein the single-state variable comprises vehicle position along the route.

21. The method of claim 12 , wherein estimating real time distance of the vehicle along the route comprises estimating real time distance of a locomotive along a locomotive track.

22. A vehicle location determination system (LDS) comprising:
a computer or processor;
a GPS processing module;
a speed integrator module;
a Kalman filter based estimator module; wherein the computer or processor is programmed to estimate real time distance of the vehicle along a route via the Kalman filter based estimator that estimates the real time location in response solely to both GPS information from the GPS processing module and vehicle speed information from the speed integrator module; and
a critical milepost module comprising a database of identifying entries that are distinct and independent

Assignees

• Bonanni Pierino Gianni
• Chan David So Keung
• Mathews Harry Kirk
• Vachhani Pramod
• Gen Electric

Inventors

• Bonanni Pierino Gianni
• Chan David So Keung
• Mathews Harry Kirk
• Vachhani Pramod

Classifications

• G01S19/47 Primary

  the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial · CPC title

• G01C21/28

  with correlation of data from several navigational instruments · CPC title

• G01S19/50

  whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks · CPC title

• G01C21/165

  combined with non-inertial navigation instruments · CPC title