Systems and methods for gyrocompass alignment using dynamically calibrated sensor data and an iterated extended kalman filter within a navigation system

• US 8,024,119 B2
• Filed: 08/14/2007
• Issued: 09/20/2011
• Est. Priority Date: 08/14/2007
• Status: Expired due to Fees

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Zeng, Yueping, Northcutt, Scott T.
• Primary Examiner(s): Hellner; Mark
• Assistant Examiner(s): DIACOU, ARI M
• Application Number:
  US11/838,811
• Publication Number:
  US 20090048779A1
• Time in Patent Office: 1,498 Days
• Field of Search: 701/216, 701/221, 702/95
• US Class Current: 701/221
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G01C 25/005 initial alignment, calibration or starting-up of inertial devices

Abstract

A navigation system and method for gyrocompass alignment in a mobile object. The system includes an inertial measurement device configured to provide a first set of sensor data and a positioning unit configured to provide a second set of data. In an example embodiment, the navigation system includes a processing device configured to receive the data sets provided by the inertial measurement device and the positioning device, and the processing device is configured to dynamically calibrate the received first data set the processing device includes a Kalman filter, and the processing device is further configured to generate a gyrocompass alignment using the first dynamically calibrated first data set, the second data set, and the Kalman filter. The method includes receiving sensor data from a plurality of sensors, dynamically calibrating at least a portion of the sensor data, and generating gyrocompass alignment information based on the dynamically calibrated sensor data.

Claims

1. A navigation system for a mobile object comprising:

an inertial measurement device configured to provide a first set of sensor data relating to operation of the mobile object; and

a processing device configured to receive the first data set and to dynamically calibrate the received first data set by comparing values in the first data set to one or more known values while the mobile object is stationary;

the processing device further configured to generate an initial heading estimate based on the dynamically calibrated first data set;

wherein dynamic calibration of the received first data set includes determining at least one of a bias or a scale factor error of the inertial measurement device;

wherein the processing device is further configured to generate a coarse gyrocompass alignment while the mobile object is stationary based on the initial heading estimate using a non-linear Kalman filter operated in a coarse-mode;

the processing device further configured to generate a fine gyrocompass alignment while the mobile object is stationary based on the coarse gyrocompass alignment using the non-linear Kalman filter operated in a fine-mode.

2. The navigation system of claim 1, wherein the non-linear Kalman filter is operated in a global iterated extended Kalman filter mode.

3. The navigation system of claim 1, wherein the Kalman filter is configured to use a virtual time step for each iteration of the Kalman filter;

wherein the virtual time step is shorter than the iteration time of the Kalman filter represented by the virtual time step.

4. The navigation system of claim 1, wherein the processing device is configured to real-time average the first set of sensor data.

5. The navigation system of claim 1, wherein the processing device is further configured to generate a gyrocompass alignment using the initial estimated heading while the mobile object is stationary using an iterated Kalman filter configured to use a virtual time step for each iteration of the Kalman filter.

6. The navigation system of claim 1, further comprising a positioning unit configured to provide a second set of data relating to position of the mobile object,

wherein the processing device is configured to update the initial heading estimate based on the dynamically calibrated first data set and the second set of data while the mobile object is moving using a Kalman filter.

7. The navigation system of claim 1, wherein the processing device is configured to dynamically calibrate the first data set by comparing values in the first data set with known gravitational acceleration and earth rate values to generate the dynamically calibrated first set of sensor data.

14. The navigation system of claim 7, wherein the processing device is configured to calibrate a z component of the first data set separately from x and y components of the first data set.

8. A method for gyrocompass alignment, the method comprising:

receiving sensor data from an inertial measurement device in a stationary mode;

dynamically calibrating at least a portion of the sensor data by comparing the received sensor data with one or more known values, wherein dynamically calibrating at least a portion of the sensor data includes determining at least one of a bias or a scale factor error of the inertial measurement device;

generating an initial heading estimate based on the dynamically calibrated sensor data;

generating gyrocompass alignment information in the stationary mode based on the initial heading estimate with a Kalman filter operated in a non-linear mode; and

displaying a navigation output based on the generated gyrocompass alignment information;

wherein generating gyrocompass alignment information with a Kalman filter operated in a non-linear mode further comprises using a virtual time step for each iteration of the Kalman filter;

wherein the virtual time step is shorter than the iteration time of the Kalman filter represented by the virtual time step.

9. The method of claim 8, wherein generating gyrocompass alignment information includes:

generating a coarse gyrocompass alignment in the stationary mode based on the initial heading estimate using a non-linear Kalman filter operated in a coarse-mode; and

generating a fine gyrocompass alignment in the stationary mode based on the coarse gyrocompass alignment using the non-linear Kalman filter operated in a fine-mode.

10. The method of claim 8, wherein generating gyrocompass alignment information in the stationary mode further includes generating gyrocompass alignment information with a Kalman filter operated in a global iterated extended Kalman filter mode.

11. The method of claim 8, wherein dynamically calibrating comprises processing the sensor data using real-time averaging to generate averaged sensor data with reduced noise.

12. The method of claim 8, wherein dynamically calibrating further comprises comparing the received sensor data with known gravitational acceleration and earth rate values.

13. The method of claim 8, further comprising:

receiving additional sensor data from a positioning unit; and

generating navigation information in a non-stationary mode based on the gyrocompass alignment information and the additional sensor using a Kalman filter.

15. A method of gyrocompass alignment, the method comprising:

receiving sensor data from an inertial measurement device in a stationary mode;

dynamically calibrating at least a portion of the sensor data by comparing the received sensor data with one or more known values;

wherein dynamically calibrating at least a portion of the sensor data includes determining at least one of a bias or a scale factor error of the inertial measurement device;

generating an initial heading estimate based on the dynamically calibrated sensor data;

generating a coarse gyrocompass alignment in the stationary mode based on the initial heading estimate using a non-linear Kalman filter operated in a coarse-mode; and

generating a fine gyrocompass alignment in the stationary mode based on the coarse gyrocompass alignment using the non-linear Kalman filter operated in a fine-mode;

wherein a virtual time step is used for each iteration of the non-linear Kalman filter in the coarse-mode and the fine-mode;

the virtual time step being shorter than the iteration time of the Kalman filter represented by the virtual time step.

16. The method of claim 15, wherein the non-linear Kalman filter is a global iterated extended Kalman filter.

17. The method of claim 15, wherein dynamically calibrating further comprises comparing the received sensor data with known gravitational acceleration and earth rate values.