Enhanced inertial system performance

• US 20070050138A1
• Filed: 08/30/2005
• Published: 03/01/2007
• Est. Priority Date: 08/30/2005
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Morgan, Kenneth, Younskevicius, Helen
• Granted Patent: US 7,860,651 B2
• Time in Patent Office: Days
• US Class Current: 701/220
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 21/188 for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems

Abstract

An inertial system is provided. The system includes at least one inertial sensor, a processing unit and a plurality of Kalman filters implemented in the processing unit. The Kalman filters receive information from the at least one inertial sensor, and at most one of the plurality of Kalman filters has processed zero velocity updates on the last cycle. The plurality of Kalman filters is used to optimize system response and performance during periods of intermittent motion.

Claims

1. An inertial system, the system comprising:

at least one inertial sensor;

a processing unit; and

a plurality of Kalman filters implemented in the processing unit, that receive information from the at least one inertial sensor, wherein at most one of the plurality of Kalman filters has processed zero velocity updates on the last cycle.

2. The system of claim 1, wherein the at least one inertial sensor is an accelerometer.

3. The system of claim 1, wherein one of the plurality of Kalman filters has not processed zero velocity updates on the last cycle.

4. The system of claim 1, wherein one of the plurality of Kalman filters has not processed zero velocity updates on multiple cycles.

5. A method of generating data in an inertial system, the method comprising:

processing multiple Kalman filters; and

selecting one of the multiple Kalman filters that did not process zero velocity updates when motion was occurring.

6. The method of claim 5, wherein processing multiple Kalman filters comprises processing measurements not associated with zero velocity updates.

7. The method of claim 5, wherein processing multiple Kalman filters comprises updating the Kalman filters.

8. The method of claim 5, wherein determining when motion was occurring comprises using a solution separation algorithm.

9. The method of claim 5, wherein determining when motion was occurring comprises taking observations and determining if they exceed a threshold.

10. The method of claim 5, further comprising copying the selected Kalman filter to all the other filters.

11. A method for generating data in an inertial system, the method comprising:

forming non-zero velocity update observations;

updating all the Kalman filters;

detecting whether there has been motion;

forming zero velocity update observations if no motion is detected;

copying all the Kalman filters that have processed one more cycle of zero velocity updates to the Kalman filters that have processed one less cycle of zero velocity updates if no motion is detected;

applying zero velocity updates to the Kalman filter that processes zero velocity updates on all cycles, when no motion is detected; and

copying the Kalman filter that did not process zero velocity updates when motion was occurring to all the Kalman filters when motion is detected.

12. The method of claim 11, wherein copying all the Kalman filters that have processed one more cycle of zero velocity updates to the Kalman filters that have processed one less cycle of zero velocity updates includes copying the Kalman filter that has processed zero velocity updates on the last cycle to the Kalman filter that has not processed zero velocity updates on the last cycle.

13. The method of claim 11, wherein copying the Kalman filter that did not process zero velocity updates when motion was occurring to all the Kalman filters comprises copying the Kalman filter that did not process zero velocity updates on the last cycle to the Kalman filter that processed zero velocity updates on the last cycle.

14. The method of claim 11, wherein copying the Kalman filter that did not process zero velocity updates when motion was occurring to all the Kalman filters comprises copying the Kalman filter that did not process zero velocity updates on the last two cycles to the Kalman filter that processed zero velocity updates on the last cycle.

15. The method of claim 11, wherein detecting whether there has been motion comprises using a solution separation algorithm.

16. The method of claim 11, wherein detecting whether there has been motion comprises taking the observations and determining if they exceed a threshold.

17. A machine-readable medium having instructions embodied thereon for a method for generating inertial measurement data, the method comprising:

forming non-zero velocity update observations;

updating all the Kalman filters;

detecting whether there has been motion;

forming zero velocity update observations if no motion is detected;

copying all the Kalman filters that have processed one more cycle of zero velocity updates to the Kalman filters that have processed one less cycle of zero velocity updates if no motion is detected;

applying zero velocity updates to the Kalman filter that processes zero velocity updates on all cycles, when no motion is detected; and

copying the Kalman filter that did not process zero velocity updates when motion was occurring to all the Kalman filters when motion is detected.

18. The machine-readable medium of claim 17, wherein detecting whether there has been motion comprises using a solution separation algorithm.

19. The machine-readable medium of claim 17, wherein detecting whether there has been motion comprises taking the observations and determining if they exceed a threshold.

20. An inertial system, the system comprising:

means for sensing the change in position of an object, the means for sensing having an output;

means for determining the position of the object from the output of the means for sensing; and

means for maintaining the accuracy of the means for determining even when zero velocity updates are processed for a moving vehicle.

21. A vehicle, the vehicle comprising:

an inertial system that generates position information, the system comprising;

at least one inertial sensor;

a processing unit;

a plurality of Kalman filters implemented in the processing unit, that receive information from the at least one inertial sensor, wherein at most one of the plurality of Kalman filters has processed zero velocity updates on the last cycle;

a guidance system that communicates with the inertial system and determines a heading for the vehicle; and

a position controller that communicates with the guidance system and physically moves the vehicle in accordance with the heading determined by the guidance system.

22. An inertial navigation system, the system comprising:

at least one sensor;

a processing unit comprising;

navigation equations that receive data from the at least one sensor and integrate the data; and

Kalman filters that receive the integrated data from the navigation equations, wherein at most one of the Kalman filters has processed zero velocity updates on the last cycle.

23. The system of claim 22, wherein the at least one sensor is at least one of an accelerometer, a gyroscope, an odometer, and a global positioning system.

24. The system of claim 22, wherein one of the Kalman filters has not processed zero velocity updates on the last cycle.

25. The system of claim 22, wherein one of the Kalman filters has not processed zero velocity updates on multiple cycles.