SYSTEM AND METHOD OF AUTO-CALIBRATION OF INERTIAL SENSORS

• US 20100088027A1
• Filed: 10/03/2008
• Published: 04/08/2010
• Est. Priority Date: 10/03/2008
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Bye, Charles T.
• Granted Patent: US 8,255,161 B2
• Time in Patent Office: Days
• US Class Current: 701/213
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 25/005 initial alignment, calibration or starting-up of inertial devices

Abstract

A system comprises at least one inertial sensor operable to provide inertial sensor data during a trip; a processing unit coupled to the at least one inertial sensor, the processing unit operable to calculate navigation data based on the inertial sensor data and to estimate error in the inertial sensor data, wherein the processing unit is further operable to adjust subsequent inertial sensor data received during the trip from the at least one inertial sensor in order to compensate for the estimated error; and a memory coupled to the navigation unit and operable to store data between power cycles; wherein the processing unit is further operable to calculate a current trip error estimate from a plurality of error estimates during the trip and to estimate a repeatability error component based on the current trip error estimate and previous trip error estimates stored in the memory; wherein the repeatability error component is stored in the memory, the processing unit being further operable to update inertial sensor data during a subsequent trip based on the repeatability error component

Claims

1. A system comprising:

at least one inertial sensor operable to provide inertial sensor data during a trip;

a processing unit coupled to the at least one inertial sensor, the processing unit operable to calculate navigation data based on the inertial sensor data and to estimate error in the inertial sensors, wherein the processing unit is further operable to adjust subsequent inertial sensor data received during the trip from the, at least one, inertial sensor to compensate for the estimated error; and

a memory coupled to the navigation unit and operable to store data between power cycles;

wherein the processing unit is further operable to calculate a current trip error estimate from a plurality of error estimates during the trip and to estimate a repeatability error component based on the current trip error estimate and previous trip error estimates stored in the memory;

wherein the repeatability error component is stored in the memory, the processing unit being further operable to update inertial sensor data during a subsequent trip based on the repeatability error component.

2. The system of claim 1, further comprising:

an aiding source coupled to the processing unit and operable to provide navigation data;

wherein the processing unit is operable to use the navigation data provided by the aiding source to estimate the error in the navigation data.

3. The system of claim 2, wherein the aiding source is a Global Positioning System (GPS) receiver.

4. The system of claim 1, wherein the processing unit is operable to estimate the error using a Kalman filter.

5. The system of claim 1, wherein the at least one inertial sensor comprises at least one linear accelerometer and at least one gyroscope.

6. The system of claim 1, wherein the at least one inertial sensor comprises a micro-electrical-mechanical system (MEMS) sensor.

7. The system of claim 1, wherein the processing unit is operable to estimate bias error and scale factor error.

8. The system of claim 1, wherein the processing unit is operable to calculate a statistically weighted trip error estimate based on the covariance of each of the plurality of error estimates during the trip.

9. The system of claim 1, wherein the processing unit is operable to compare the covariance of each error estimate with a threshold value, wherein error estimates having a covariance above the threshold value are discarded and error estimates having a covariance below the threshold are used to adjust subsequent inertial sensor data.

10. The system of claim 1, wherein the processing unit is operable to estimate a repeatability error component based on the current trip error estimate and a moving window of previous trip error estimates.

11. A method of calibrating an inertial sensor, the method comprising:

receiving inertial sensor data from the inertial sensor;

calculating navigation data based on the received inertial sensor data;

estimating error in the calculated navigation data;

adjusting subsequent received inertial sensor data to compensate for estimated errors;

calculating a trip error estimate from a plurality of error estimates during a trip;

estimating a repeatability error component based on the current trip error estimate and previous trip error estimates;

storing the repeatability error component in a memory; and

adjusting inertial sensor data during a subsequent trip based, at least in part, on the repeatability error component.

12. The method of claim 11, wherein estimating error in the calculated navigation data comprises estimating error in the calculated navigation data based on navigation data provided by an aiding source.

13. The method of claim 11, wherein estimating error in the calculated navigation data comprises estimating bias error and scale factor error.

14. The method of claim 11, wherein adjusting subsequent received inertial sensor data to compensate for estimated errors comprises:

comparing the covariance of each estimated error with a threshold value;

discarding estimated errors having a covariance above the threshold value; and

adjusting subsequent received inertial sensor data with estimated errors having a covariance equal to or below the threshold value.

15. The method of claim 11, wherein calculating a trip error estimate from a plurality of error estimates during the trip comprises calculating a weighted average of the plurality of error estimates based on the covariance of each of the plurality of error estimates.

16. The method of claim 11, wherein estimating a repeatability error component comprises estimating a repeatability error component based on the current trip error estimate and a moving window of previous trip error estimates.

17. A program product comprising program instructions embodied on a processor-readable medium for execution by a programmable processor, wherein the program instructions are operable to cause the programmable processor to:

calculate navigation data based on inertial sensor data received from at least one inertial sensor;

estimate error in the calculated navigation data based on navigation data received from an aiding source;

adjust subsequent received inertial sensor data to compensate for estimated errors;

calculate a trip error estimate from a plurality of error estimates during a trip;

retrieve a plurality of previous trip error estimates from a memory device;

estimate a repeatability error component based on the calculated trip error estimate and the plurality of previous trip error estimates;

storing the repeatability error component in the memory device; and

adjusting inertial sensor data during a subsequent trip based, at least in part, on the repeatability error component.

18. The program product of claim 17, wherein the program instructions are further operable to cause the programmable processor to retrieve only a set number of the most recent previous trip error estimates from the memory device to use in estimating the repeatability error component.

19. The program product of claim 17, wherein the program instructions are further operable to cause the programmable processor to estimate both bias error and scale factor error in the inertial sensor data.

20. The program product of claim 17, wherein the program instructions are further operable to cause the programmable processor to:

compare each estimated error'"'"'s covariance with a threshold value;

discard estimated errors having a covariance above the threshold value; and

adjust subsequent received inertial sensor data with estimated errors having a covariance equal to or below the threshold value.