TIGHTLY-COUPLED GNSS/IMU INTEGRATION FILTER HAVING SPEED SCALE-FACTOR AND HEADING BIAS CALIBRATION

• US 20100109950A1
• Filed: 11/04/2009
• Published: 05/06/2010
• Est. Priority Date: 11/06/2008
• Status: Active Grant

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Roh, June Chul
• Granted Patent: US 8,447,517 B2
• Time in Patent Office: Days
• US Class Current: 701/216
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

Embodiments of the invention provide a tightly-coupled integration filter for inertial sensor-assisted GNSS (global navigation satellite system) receiver. The inertial measurement unit (IMU) contains inertial sensors such as accelerometer, magnetometer, and/or gyroscopes. Embodiments include blending filter based on extended Kalman filter (EKF), which optimally integrates the IMU navigation data with all other satellite measurements (tightly-coupled integration filter). The proposed blending filter includes two states for estimating/compensating the speed scale-factor and the heading bias in the INS measurement.

Claims

1. An apparatus comprising:

an integration filter for a sensor-assisted global navigation satellite system (GNSS) receiver

a GNSS measurement engine for providing GNSS measurement data to said integration filter;

an inertial measurement unit (IMU); and

an inertial navigation system (INS) block for calculating INS navigation information using a plurality of inertial sensor outputs,

wherein said integration filter performs a blending operation for combining said GNSS measurement data and said INS navigation information, and for estimating and compensating a speed scale-factor and a heading bias in the INS measurement.

2. The apparatus of claim 1, wherein said integration filter is selected from the list consisting of:

a Kalman Filter,

a Linearized Kalman Filter (LKF),

an Extended Kalman Filter (EKF),

an Unscented Kalman Filter (UKF),

a particle filter,

a least squares filter, and

an intelligent/learning filter.

3. The apparatus of claim 1, wherein said measurement data comprises a pseudorange measurement and a delta range measurement for at least one satellite.

4. The apparatus of claim 1, wherein measurement engine providing measurement noise variance for said pseudorange measurement and said delta range measurement.

5. The apparatus of claim 1, wherein the INS navigation information comprises one of:

a first set comprising at least one of a position estimate and a velocity estimate based on inertial sensor output; and

a second set comprising at a speed estimate and a heading estimate based on inertial sensor output.

6. The apparatus of claim 5, wherein said INS user velocity data from said INS is synchronized to GNSS measurement data.

7. The apparatus of claim 1, wherein INS providing measurement noise variance for said INS user velocity data.

8. The apparatus of claim 1, wherein a speed scale-factor and/or a heading bias in the INS measurement are included in state variables of said integration filter, and said speed scale-factor and/or said heading bias are estimated and compensated in calculating the blended navigation information using the GNSS measurements and INS measurements.

9. The apparatus of claim 8, wherein integration filter processes said INS user velocity data from said INS in said measurement equation of said integration filter using a method comprising:

a plurality of INS measurements in a local navigation coordinate are included in said measurement equation in a way that said INS measurements are a function of velocity variables and speed scale-factor and/or heading bias variables of an integration filter state with a plurality of measurement noises.

10. The apparatus of claim 9, wherein a vertical INS measurement from said plurality of INS measurements is set to zero.

11. The apparatus of claim 9, wherein said integration filter does not integrate said INS measurements when GNSS measurements are reliable when GNSS signal condition is good.

12. The apparatus of claim 9, said integration filter does not update said speed scale-factor and/or said heading bias when the GNSS measurement is unreliable.

13. The apparatus of claim 9, wherein a plurality of process noise variances for said speed scale-factor and/or said heading bias are set to small numbers when the quality of the GNSS measurement is not good, so that state variables for speed scale-factor and/or said heading bias are effectively not changing over time.

14. The apparatus of claim 13, wherein the time instance for changing the process noise variances for the speed scale-factor and/or said heading bias states is determined based on the quality of the GNSS measurement, e.g., when GNSS position uncertainty is larger than a threshold during a GNSS outage.

15. A method of calibration comprising:

creating a coordinate transformation matrix using a latest position fix (latitude and longitude);

transforming said state variables (for user velocity) to a local navigation coordinate using said coordinate transformation matrix;

including a plurality of INS measurements in said local navigation coordinate in a measurement equation in a way that said INS measurements are a function of a plurality of velocity variables and speed scale-factor and/or heading bias variables of an integration filter state with a plurality of measurement noises;

estimating said state variables (which include speed scale-factor and/or heading bias) of said integration filter.

outputting a blended calibrated position fix.

16. The method of claim 15, wherein said speed scale-factor and/or said heading bias are not updated when the GNSS measurement is unreliable.

17. The method of claim 15, further comprising setting a plurality of process noise variances for said speed scale-factor and/or said heading bias to small numbers when the quality of the GNSS measurement is not good, so that state variables for speed scale-factor and/or said heading bias are effectively not changing over time.

18. The method of claim 15, further comprising determining a time instance for changing the process noise variances for the speed scale-factor and/or said heading bias states based on the quality of the GNSS measurement, e.g., when GNSS position uncertainty is larger than a threshold during a GNSS outage.

19. The method of claim 18, wherein said time instance for changing the process noise variances for the speed scale-factor and/or said heading bias states is determined when GNSS position uncertainty is larger than a threshold during a GNSS outage.

20. The method of claim 15, wherein determination of said measurement noise variance for INS user velocity is selected from the group consisting of:

accuracy of sensors, mounting condition of IMU, and dynamics of the receiver.

21. A method of calibration comprising:

creating a coordinate transformation matrix using a latest position fix (latitude and longitude);

transforming said state variables (for user velocity) to a local navigation coordinate using said coordinate transformation matrix;

including a heading measurement using an angle of said INS velocity vector on horizontal navigation plane;

estimating said state variables (which include speed scale-factor and/or heading bias) of said integration filter.

outputting a blended calibrated position fix.

22. A method of calibration comprising:

creating a coordinate transformation matrix using a latest position fix (latitude and longitude);

transforming said state variables (for user velocity) to a local navigation coordinate using said coordinate transformation matrix;

including a speed measurement using a magnitude of an INS velocity vector on horizontal navigation plane;

estimating said state variables (which include speed scale-factor and/or heading bias) of said integration filter.

outputting a blended calibrated position fix.

23. The method of claim 22, further comprising including a heading measurement using an angle of said INS velocity vector on horizontal navigation plane.