Methods and Systems for Evaluating the Performance of MEMS-based Inertial Navigation Systems

• US 20090319228A1
• Filed: 04/25/2008
• Published: 12/24/2009
• Est. Priority Date: 04/25/2007
• Status: Active Grant

• Current Assignee: UTI Limited Partnership
• Original Assignee: UTI Limited Partnership
• Inventors: Nassar, Sameh, Niu, Xiaoji, El-Sheimy, Naser
• Granted Patent: US 8,359,182 B2
• Time in Patent Office: Days
• US Class Current: 702/182
• CPC Class Codes: G01C 25/005 initial alignment, calibration or starting-up of inertial devices

Abstract

Disclosed are systems and methods for evaluating the navigation performance of MEMS inertial sensors. The method uses MEMS inertial sensors static data signals to estimate the static sensor errors and combines them with a reference kinematic signal obtained through field testing of a high-grade inertial sensor. Such emulated field data may then be processed with the corresponding GPS data collected in the same or different field test to evaluate the navigation performance of the MEMS inertial sensors.

Claims

1. A method for evaluating performance of MEMS inertial sensors comprising:

field-testing a reference inertial sensor to generate reference field-test data;

lab-testing a plurality of MEMS inertial sensors to generate static error data for each tested MEMS inertial sensor;

combining the reference field-test data with the static error data for each MEMS inertial sensor to generate an emulated field data for each MEMS inertial sensor; and

processing the emulated field data for each MEMS inertial sensor with a GPS field data to evaluate performance of the plurality of MEMS inertial sensors.

2. The method of claim 1, wherein the reference inertial sensor includes a high-grade inertial sensor and the MEMS inertial sensors include low-grade inertial sensors.

3. The method of claim 1, wherein the field test includes testing of one or more of general trajectories, driving conditions and GPS signal conditions.

4. The method of claim 1, wherein the lab-testing includes varying temperature conditions of the tested MEMS inertial sensors.

5. The method of claim 1, wherein the static error data is being collected for a period longer than the duration of the field test.

6. The method of claim 1, further comprising removing gravity effect from the static error data before combining it with the reference field-test data.

7. The method of claim 1, further comprising removing the Earth rotation rate effect from the static error data before combining it with the reference field-test data.

8. A method for evaluating performance of MEMS inertial sensors comprising:

field-testing a reference inertial sensor to generate reference field-test data;

testing a plurality of MEMS inertial sensors to generate static and dynamic error data for each tested MEMS inertial sensor;

combining the reference field-test data with the static and dynamic error data for each MEMS inertial sensor to generate an emulated field data for each MEMS inertial sensor; and

processing the emulated field data for each MEMS inertial sensor with a GPS field data to evaluate performance of the plurality of MEMS inertial sensors.

9. The method of claim 8, wherein the reference inertial sensor includes a high-grade inertial sensor and the MEMS inertial sensors include low-grade inertial sensors.

10. The method of claim 8, wherein the field test includes testing of one or more of general trajectories, driving conditions and GPS signal conditions.

11. The method of claim 8, wherein the lab-testing includes varying temperature conditions of the tested MEMS inertial sensors.

12. The method of claim 8, wherein the static and dynamic error data is being collected for a period longer than the duration of the field test.

13. The method of claim 8, further comprising removing gravity effect from the static and dynamic error data before combining it with the kinematic field-test data.

14. The method of claim 8, further comprising removing the Earth rotation rate effect from the static and dynamic error data before combining it with the kinematic field-test data.

15. A method for evaluating performance of MEMS inertial sensors comprising:

receiving kinematic field-test data of a reference inertial sensor;

receiving static error data for a MEMS inertial sensor;

combining the kinematic field-test data with the static error data for the MEMS inertial sensor to generate an emulated field data for the MEMS inertial sensor; and

processing the emulated field data for the MEMS inertial sensor with a GPS field data to evaluate performance of the MEMS inertial sensors.

16. The method of claim 15, wherein the reference inertial sensor includes a high-grade inertial sensor and the MEMS inertial sensors include low-grade inertial sensors.

17. The method of claim 15, wherein the static error data exceeds the kinematic field-test data.

18. The method of claim 15, wherein the receiving static error data further includes receiving dynamic error data for a MEMS inertial sensor.

19. The method of claim 18, wherein the combining the kinematic field-test data with the static error data further includes combining the dynamic error data.

20. The method of claim 19, further comprising removing gravity effect from the static and dynamic error data before combining it with the kinematic field-test data.

21. The method of claim 19, further comprising removing the Earth rotation rate effect from the static and dynamic error data before combining it with the kinematic field-test data.

22. A system for evaluating performance of MEMS inertial sensors comprising:

a reference inertial sensor operable to generate reference field-test data;

a plurality of MEMS inertial sensors operable to generate static error data;

a signal combiner operable to combine the reference field-test data with the static error data for each MEMS inertial sensor to generate emulated field data for each MEMS inertial sensor; and

processor configurable to evaluate performance of the plurality of MEMS inertial sensors by processing the emulated field data for each MEMS inertial sensor with a GPS field data.

23. The system of claim 22, wherein the reference inertial sensor includes a high-grade inertial sensor and the MEMS inertial sensors include low-grade inertial sensors.

24. The system of claim 22, wherein the static error data exceeds the reference field-test data.

25. The system of claim 22, wherein the plurality of MEMS inertial sensors are further operable to generate dynamic error data.

26. The system of claim 25, wherein the signal combiner is further operable to combine the reference field-test data, the static error data and the dynamic error data.