System and method for estimating heading misalignment

• US 9,752,879 B2
• Filed: 04/14/2015
• Issued: 09/05/2017
• Est. Priority Date: 04/14/2015
• Status: Active Grant

• Current Assignee: Invensense Incorporated (TDK Corporation)
• Original Assignee: Invensense Incorporated (TDK Corporation)
• Inventors: Al-Hamad, Amr, Ali, Abdelrahman, Georgy, Jacques
• Primary Examiner(s): BUTLER, RODNEY ALLEN
• Application Number:
  US14/686,441
• Publication Number:
  US 20160305782A1
• Time in Patent Office: 875 Days
• CPC Class Codes: G01C 21/183 Compensation of inertial measurements, e.g. for temperature effects

Abstract

This disclosure relates to heading misalignment estimation with a portable device and more specifically to estimating misalignment between a portable device having a sensor assembly and a platform transporting the portable device when the platform is undergoing motion having periodic characteristics. In one aspect, a suitable method includes obtaining inertial sensor data for the portable device and determining an effective frequency characteristic of the inertial sensor data representing the motion having periodic characteristics. The inertial sensor data may be processed using the determined frequency characteristic so that a heading misalignment may be estimated between the heading of the portable device and the platform.

Claims

1. A portable device, the device being within a platform, the device comprising a sensor assembly integrated with the portable device that outputs inertial sensor data and a heading misalignment estimator configured to:

determine an effective frequency characteristic of the inertial sensor data, wherein the effective frequency characteristic of the inertial sensor data represents periodic characteristics of motion of the platform;

process the inertial sensor data based at least in part on the determined frequency characteristic, wherein processing the inertial sensor data comprises reducing signal artifacts; and

estimate a heading misalignment between a heading of the portable device and the platform using the processed inertial sensor data, wherein estimating the heading misalignment is based at least in part on a maximum variance of the processed inertial sensor data.

2. The device of claim 1, wherein the heading misalignment estimator is configured to determine the effective frequency characteristic of the inertial sensor data by performing a time domain analysis.

3. The device of claim 1, wherein the heading misalignment estimator is configured to determine the effective frequency characteristic of the inertial sensor data by performing a frequency domain analysis.

4. The device of claim 1, further comprising a band pass filter, wherein the heading misalignment estimator processes the inertial sensor data by applying the band pass filter.

5. The device of claim 1, wherein the heading misalignment estimator is further configured to estimate the heading misalignment based at least in part on a maximum variance of the processed inertial sensor data.

6. The device of claim 1, wherein the heading misalignment estimator is further configured to resolve an ambiguity in the estimated heading misalignment.

7. The device of claim 1, wherein the sensor assembly comprises an inertial sensor implemented as a Micro Electro Mechanical System (MEMS).

8. A method for navigation with a portable device associated with a platform undergoing motion having periodic characteristics, the method comprising:

obtaining inertial sensor data for the portable device;

determining an effective frequency characteristic of the inertial sensor data;

processing the inertial sensor data based at least in part on the determined frequency characteristic, wherein processing the inertial sensor data comprises reducing signal artifacts; and

estimating a heading misalignment between a heading of the portable device and the platform using the processed inertial sensor data, wherein estimating the heading misalignment is based at least in part on a maximum variance of the processed inertial sensor data.

9. The method of claim 8, wherein processing the inertial sensor data comprises processing accelerometer data.

10. The method of claim 9, wherein determining the effective frequency characteristic of the inertial sensor data comprises performing a time domain analysis.

11. The method of claim 10, wherein the time domain frequency analysis is performed on a periodic signal selected from at least one of accelerometer data, leveled accelerometer data, gyroscope data, leveled gyroscope data, roll data for the portable device and pitch data for the portable device.

12. The method of claim 11, further comprising classifying at least one of a use case and a motion mode for the portable device and selecting the periodic signal for performing the time domain analysis based at least in part on the classification.

13. The method of claim 11, wherein the time domain analysis calculates peak frequency in the obtained inertial sensor data.

14. The method of claim 13, comprising processing the sensor data with the calculated peak frequency to reduce the artifacts.

15. The method of claim 14, further comprising determining coefficients at the calculated peak frequency.

16. The method of claim 15, wherein processing the inertial sensor data comprises applying the determined coefficients to reconstruct the inertial sensor data to reduce the artifacts.

17. The method of claim 9, wherein determining the effective frequency characteristic of the inertial sensor data comprises performing a frequency domain analysis.

18. The method of claim 17, wherein the frequency domain analysis is performed on a periodic signal selected from at least one of accelerometer data, leveled accelerometer data, gyroscope data, leveled gyroscope data, roll data for the portable device and pitch data for the portable device.

19. The method of claim 18, further comprising classifying at least one of a use case and a motion mode for the portable device and selecting the periodic signal for performing the frequency domain analysis based at least in part on the classification.

20. The method of claim 18, wherein the frequency domain analysis comprises a Fast Fourier Transform (FFT).

21. The method of claim 20, further comprising determining a maximum amplitude frequency component corresponding to the effective frequency characteristic.

22. The method of claim 21, comprising processing the sensor data with the determined maximum amplitude frequency component to reduce the artifacts.

23. The method of claim 22, further comprising determining coefficients at the maximum amplitude frequency component.

24. The method of claim 23, wherein processing the inertial sensor data comprises applying the determined coefficients to reconstruct the inertial sensor data to reduce the artifacts.

25. The method of claim 9, wherein processing the inertial sensor data comprises applying a band pass filter.

26. The method of claim 25, further comprising classifying at least one of a use case and a motion mode for the portable device and selecting the band pass filter based at least in part on the classification.

27. The method of claim 8, further comprising determining the maximum variance of the processed inertial sensor data using a Principle Component Analysis (PCA).

28. The method of claim 8, further comprising determining the maximum variance of the processed inertial sensor data using a binary search.

29. The method of claim 8, further comprising determining the maximum variance of the processed inertial sensor data by analytically determining an along-track axis representing a minimum sum of squared distances.

30. The method of claim 8, further comprising resolving an ambiguity in the estimated heading misalignment.

31. The method of claim 30, wherein resolving the estimated heading misalignment ambiguity comprises assessing a difference between two angles calculated between a vertical acceleration of the portable device and an along-track axis acceleration of the portable device.

32. The method of claim 31, wherein the difference between two angles calculated between a vertical acceleration of the portable device and the along-track axis acceleration of the portable device is determined as a difference between an angle representing a phase shift between a lagging vertical acceleration signal and a leading along-track axis acceleration signal and an angle representing a phase shift between a leading vertical acceleration signal and a lagging along-track axis acceleration signal.

33. The method of claim 30, wherein resolving estimated heading misalignment ambiguity comprises assessing phase shift between a vertical acceleration of the portable device and an along-track axis acceleration of the portable device.

34. The method of claim 30, wherein resolving the estimated heading misalignment ambiguity comprises determining effective motion parameters based at least in part on a maximum variance between a vertical acceleration of the portable device and an along-track axis acceleration of the portable device.

35. The method of claim 34, further comprising determining the maximum variance between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device using a Principle Component Analysis (PCA).

36. The method of claim 34, further comprising determining the maximum variance between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device by calculating a slope of a principle axis between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device, wherein the principal axis is obtained using a binary search.

37. The method of claim 34, further comprising analytically determining the maximum variance between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device by calculating a slope of a principle axis between the vertical acceleration of the portable device and the along-track axis acceleration of the portable device, wherein the principal axis represents a minimum sum of squared distances.