Methods and systems for evaluating the performance of MEMS-based inertial navigation systems

• US 8,359,182 B2
• Filed: 04/25/2008
• Issued: 01/22/2013
• Est. Priority Date: 04/25/2007
• Status: Expired due to Fees

• Current Assignee: UTI Limited Partnership
• Original Assignee: UTI Limited Partnership
• Inventors: Niu, Xiaoji, El-Sheimy, Naser, Nassar, Sameh
• Primary Examiner(s): Nghiem, Michael
• Assistant Examiner(s): Ngon, Ricky
• Application Number:
  US12/109,923
• Publication Number:
  US 20090319228A1
• Time in Patent Office: 1,733 Days
• Field of Search: 702 33- 35, 702/57, 702 92- 96, 702/99, 702/104, 702/113, 702/116, 702/141, 702/142, 702/145, 702/150, 702/182, 702/183, 701213-214, 701220-221, 701 29- 35
• 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 including a real kinematic inertial signal from the field-testing, operating conditions experienced by the reference inertial sensor during the field-testing, and GPS signal conditions experienced by the reference inertial sensor during the field-testing;

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

calculating a sum of the reference field-test data and the static error data for each MEMS inertial sensor to generate an emulated field data for each MEMS inertial sensor, and calculating an average position drift during a GPS signal outage; and

processing, with a processor, the emulated field data for each MEMS inertial sensor with a GPS field data to evaluate performance of the plurality of MEMS inertial sensors for characterizing how the MEMS inertial sensor will operate during varied GPS signal conditions.

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 the static error data 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 the static error data 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 including a real kinematic inertial signal from the field-testing, operating conditions experienced by the reference inertial sensor during the field-testing, and GPS signal conditions experienced by the reference inertial sensor during the field-testing;

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

calculating a sum of the reference field-test data and the static and dynamic error data for each MEMS inertial sensor to generate an emulated field data for each MEMS inertial sensor, and calculating an average position drift during a GPS signal outage; and

processing, with a processor, the emulated field data for each MEMS inertial sensor with a GPS field data to evaluate performance of the plurality of MEMS inertial sensors for characterizing how the MEMS inertial sensor will operate during varied GPS signal conditions.

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 the static and dynamic error data 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 the static and dynamic error data 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;

calculating a sum of the kinematic field-test data and the static error data for the MEMS inertial sensor to generate an emulated field data for the MEMS inertial sensor, and calculating an average position drift during a GPS signal outage; and

processing, with a processor, the emulated field data for the MEMS inertial sensor with a GPS field data to evaluate performance of the MEMS inertial sensors for characterizing how the MEMS inertial sensor will operate during varied GPS signal conditions.

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, further comprising combining the dynamic error data with the kinematic field-test data and the static error data.

20. The method of claim 19, further comprising removing gravity effect from the static and dynamic error data before combining the static and dynamic error data 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 the static and dynamic error data 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 calculate a sum of the reference field-test data and the static error data for each MEMS inertial sensor to generate emulated field data for each MEMS inertial sensor; and

a 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, wherein an average position drift during a GPS signal outage is used to characterize how the MEMS inertial sensor will operate during varied GPS signal conditions.

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.