Optimal combination of satellite navigation system data and inertial data

• US 8,589,072 B2
• Filed: 04/13/2012
• Issued: 11/19/2013
• Est. Priority Date: 04/13/2011
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Morgan, Kenneth Steven
• Primary Examiner(s): Tarcza, Thomas
• Assistant Examiner(s): Dunn, Alex C
• Application Number:
  US13/446,093
• Publication Number:
  US 20120265440A1
• Time in Patent Office: 585 Days
• Field of Search: 701/472
• US Class Current: 701/472
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 21/188 for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems

Abstract

A navigation system comprises at least one processor, a satellite navigation receiver operatively coupled to the processor and configured to receive a plurality of navigation signals from one or more navigation satellites, and an inertial measurement unit operatively coupled to the processor and configured to generate inertial measurement data. An extended Kalman filter is configured to receive the plurality of navigation signals and the inertial measurement data. A processor readable storage medium includes instructions executable by the processor to combine the plurality of satellite signals with the inertial measurement data in the extended Kalman filter to generate a navigation solution. The plurality of navigation signals includes L1 pseudorange measurements, L2 pseudorange measurements, L1 accumulated carrier phase measurements, and L2 accumulated carrier phase measurements.

Claims

1. A navigation system, comprising:

at least one processor;

a satellite navigation receiver operatively coupled to the processor and configured to receive a plurality of navigation signals from one or more navigation satellites;

an inertial measurement unit operatively coupled to the processor and configured to generate inertial measurement data;

an extended Kalman filter configured to receive the plurality of navigation signals and the inertial measurement data, wherein the extended Kalman filter models a range bias for ionospheric error; and

a processor readable storage medium including instructions executable by the processor to combine the plurality of satellite signals with the inertial measurement data in the extended Kalman filter to generate an absolute navigation solution;

wherein the plurality of navigation signals includes L1 pseudorange measurements, L2 pseudorange measurements, L1 accumulated carrier phase measurements, and L2 accumulated carrier phase measurements.

2. The navigation system of claim 1, further comprising a navigation propagation unit configured to receive the inertial measurement data and initial navigation state conditions data, and output navigation state data and an inertial navigation solution (INS) state transition matrix to the extended Kalman filter.

3. The navigation system of claim 1, wherein the extended Kalman filter is further configured to receive pressure altitude data.

4. The navigation system of claim 2, wherein the extended Kalman filter is further configured to output navigation state corrections data in a feedback loop to an input of the navigation propagation unit.

5. The navigation system of claim 4, wherein the navigation propagation unit comprises:

a navigation state corrections module configured to receive navigation state data, and the navigation state corrections data;

an integrator configured to receive updated navigation state data from the navigation state corrections module; and

a state transition matrix integration module configured to receive navigation state data from the integrator and generate the INS state transition matrix.

6. The navigation system of claim 5, wherein the integrator comprises:

a quaternion integration module configured to receive gyroscope data and earth rate data, and output a new sensor to navigation frame quaternion;

a velocity integration module configured to receive accelerometer data and gravity data, and output a new navigation frame velocity; and

a position integration module configured to receive a time step, and output a new navigation frame position;

wherein the new sensor to navigation frame quaternion is input into the velocity integration module, and the new navigation frame velocity is input into the velocity integration module.

7. The navigation system of claim 5, wherein the extended Kalman filter includes a processing unit configured to receive navigation state data output by the integrator, GPS measurement data, the INS state transition matrix, and pressure altitude data.

8. The navigation system of claim 7, wherein the processing unit generates the navigation state corrections data, updated EKF data, and updated measurement state data.

9. The navigation system of claim 8, wherein the processing unit comprises:

an EKF state propagation module configured to generate the INS state transition matrix; and

an EKF measurement processing module configured for non-GPS measurement processing, GPS measurement processing, post measurement residual screening, and second pass measurement processing.

10. A navigation method, comprising:

receiving navigation satellite measurement data in a satellite receiver from one or more navigation satellites, the satellite measurement data including L1 pseudorange measurements, L2 pseudorange measurements, L1 accumulated carrier phase measurements, and L2 accumulated carrier phase measurements;

receiving inertial measurement data in a processing device from one or more inertial sensors;

processing the satellite measurement data with the inertial measurement data in the processing device using an extended Kalman filter, wherein the extended Kalman filter models a range bias for ionospheric error; and

generating an absolute navigation solution with the processing device based on the processed satellite measurement data and inertial measurement data.

11. The method of claim 10, further comprising:

receiving inertial navigation state conditions data; and

generating an integrated inertial navigation solution state transition matrix in the extended Kalman filter.

12. The method of claim 10, wherein for each visible satellite with valid L1 or L2 pseudorange measurements, the extended Kalman filter performs a process comprising:

forming a range vector from an antenna to the satellite;

forming a magnitude of the range vector;

forming a unit vector to the satellite;

forming an expected GPS measurement from the magnitude of the range vector and a current clock bias;

forming a GPS measurement residual by subtracting the expected GPS measurement from the L1 pseudorange measurement or the L2 pseudorange measurement;

forming a sensitivity vector for the L1 pseudorange measurement or the L2 pseudorange measurement, derived from a partial derivative of a measurement equation with respect to modeled states; and

forming observation noise for the L1 pseudorange measurement or the L2 pseudorange measurement, accounting for expected GPS pseudorange white noise and multipath.

13. The method of claim 12, wherein the modeled states include position, clock bias, pseudorange bias, and the ionospheric error.

14. The method of claim 12, further comprising:

after a clock bias initialization, performing a measurement residual test by comparing expected measurement variance with actual residual; and

for all measurements that pass the residual test, updating a state vector, a U matrix, and a D vector using a UD measurement update algorithm.

15. The method of claim 10, wherein for each visible satellite with valid L1 or L2 accumulated carrier phase measurements, the extended Kalman filter performs a process comprising:

forming a range vector from an antenna to the satellite;

forming a magnitude of the range vector;

forming a unit vector to the satellite;

forming an expected GPS measurement from the magnitude of the range vector and a current clock bias;

forming a GPS measurement residual by subtracting the expected GPS measurement from the L1 accumulated carrier phase measurement or the L2 accumulated carrier phase measurement;

forming a sensitivity vector for the L1 accumulated carrier phase measurement or the L2 accumulated carrier phase measurement, derived from a partial derivative of a measurement equation with respect to modeled states for L1 or L2 accumulated carrier phase measurements; and

forming observation noise for the L1 accumulated carrier phase measurement or the L2 accumulated carrier phase measurement, accounting for expected GPS pseudorange white noise.

16. The method of claim 15, wherein the modeled states for L1 accumulated carrier phase measurements include position, clock bias, pseudorange bias, L1 accumulated phase offset, and the ionospheric error.

17. The method of claim 15, wherein the modeled states for L2 accumulated carrier phase measurements include position, clock bias, pseudorange bias, L2 accumulated phase offset, and the ionospheric error.

18. The method of claim 15, further comprising:

after a clock bias initialization, performing a measurement residual test by comparing expected measurement variance with actual residual; and

for all measurements that pass the residual test, updating a state vector, a U matrix, and a D vector using a UD measurement update algorithm.

19. A computer program product, comprising:

a non-transitory computer readable medium including instructions stored thereon executable by a processor to perform a navigation method comprising;

receiving navigation satellite measurement data from one or more navigation satellites, the satellite measurement data including L1 pseudorange measurements, L2 pseudorange measurements, L1 accumulated carrier phase measurements, and L2 accumulated carrier phase measurements;

receiving inertial measurement data from one or more inertial sensors;

processing the satellite measurement data with the inertial measurement data in an extended Kalman filter, wherein the extended Kalman filter models a range bias for ionospheric error; and

generating an absolute navigation solution based on the processed satellite measurement data and inertial measurement data.

20. The computer program product of claim 19, wherein the method further comprises:

receiving inertial navigation state conditions data; and

generating an integrated inertial navigation solution state transition matrix in the extended Kalman filter.