INTEGRATED INERTIAL/GPS NAVIGATION SYSTEM

• US 20020109628A1
• Filed: 03/23/2001
• Published: 08/15/2002
• Est. Priority Date: 04/26/2000
• Status: Active Grant

• Current Assignee: Northrop Grumman Systems Corporation (Northrop Grumman Corporation)
• Original Assignee: Litton Systems Incorporated (Northrop Grumman Corporation)
• Inventors: Diesel, John W.
• Granted Patent: US 6,417,802 B1
• Time in Patent Office: Days
• US Class Current: 342/357.14
• CPC Class Codes: G01S 19/26 involving a sensor measurement for aiding acquisition or tracking

Abstract

The invention is a method and apparatus for adjusting the phase and frequency of a received GPS signal in an inertial-GPS navigation system by inputting a delta-phase at delta-time intervals into the received satellite signal. The method comprises the steps of determining two delta-phase components: (a) a Kalman delta-phase component derived from a plurality of candidate Kalman delta-phase components obtained by performing a first set of more than one Kalman filter processes and (b) an IMU delta-phase component derived from the IMU outputs and a direction-cosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertial-GPS navigation system.

Claims

1. A method for adjusting the phase and frequency of a received GPS signal in an inertial-GPS navigation system comprising an inertial measurement unit (IMU) and a GPS receiver, the GPS receiver having an input port for inputting a delta-phase at delta-time intervals into a received satellite signal, the GPS receiver having an output port for outputting a carrier phase error, the carrier phase error being the difference between the actual phase of the received GPS signal and a reference value, the GPS receiver having an output port for outputting a pseudorange associated with the received GPS signal, the IMU having an output port for outputting acceleration and angular velocity of the IMU, the delta-phase being the sum of at least two delta-phase components, the method comprising the step:

(a) determining a Kalman delta-phase component, a plurality of candidate Kalman delta-phase components being obtained by performing a first set of more than one Kalman filter processes, the inputs to each first-set Kalman filter process including the carrier phase error and a position vector associated with the IMU, a candidate Kalman delta-phase component being produced as an output of each first-set Kalman filter process, the Kalman delta-phase component being determined from a candidate Kalman delta-phase components selected from the plurality of candidate Kalman delta-phase components by applying a predetermined Kalman delta-phase component selection rule;

(b) determining an IMU delta-phase component from the EMU outputs and a direction-cosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertial-GPS navigation system.

2. The method of claim 1 wherein step (a) comprises the step:

(a1) identifying the present value of the Kalman delta-phase component with a future value of the selected candidate delta-phase component.

3. The method of claim 1 wherein step (a) comprises the steps:

(a1) obtaining a plurality of candidate error-state vectors by performing a second set of more than one Kalman filter processes, a candidate error-state vector being produced as an output of each second-set Kalman filter process utilizing a plurality of prior pseudorange values and the present position vector;

(a2) selecting the error-state vector from the candidate error-state vectors by applying a predetermined error-state vector selection rule;

(a3) determining the position vector using IMU outputs and the error-state vector.

4. The method of claim 3 wherein in step (a1) each Kalman filter process in the second set is tuned to recognize either (1) the absence of any failure conditions or (2) the presence of a particular failure condition, a failure condition being the failure of a group of one or more functional elements of the inertial-GPS navigation system to perform within specifications.

5. The method of claim 3 wherein in step (a2) the selected error-state vector is the error-state vector produced by the Kalman filter process producing a value of a failure-condition quantity that is statistically distinguishable from the values of the failure-condition quantity produced by the other second-set Kalman filter processes.

6. The method of claim 5 whereby the failure-condition quantity is derived from weighted residuals summed over a plurality of Kalman filter process update cycles.

7. The method of claim 1 wherein step (b) comprises the steps:

(b1) estimating future values of IMLJ acceleration and angular velocity from the IMU outputs;

(b2) translating the future values of IMW acceleration and angular velocity into the IMU delta-phase component utilizing the direction-cosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertial-GPS navigation system.

8. The method of claim 7 wherein step (b2) comprises the steps:

(b2-1) obtaining a plurality of candidate error-state vectors by performing a second set of more than one Kalman filter processes, a candidate error-state vector being produced as an output of each second-set Kalman filter process utilizing a plurality of prior pseudorange values and the present position vector;

(b2-2) selecting the error-state vector from the candidate error-state vectors by applying a predetermined error-state vector selection rule;

(b2-3) determining the direction-cosine matrix using IMU outputs and the error-state vector.

9. The method of claim 8 wherein in step (b2-1) each Kalman filter process in the second set is tuned to recognize either (1) the absence of any failure conditions or (2) the presence of a particular failure condition, a failure condition being the failure of a group of one or more functional elements of the inertial-GPS navigation system to perform within specifications.

10. The method of claim 8 wherein in step (b2-2) the selected error-state vector is the error-state vector produced by the Kalman filter process producing a value of a failure-condition quantity that is statistically distinguishable from the values of the failure-condition quantity produced by the other second-set Kalman filter processes.

11. The method of claim 10 whereby the failure-condition quantity is derived from weighted residuals summed over a plurality of Kalman filter process update cycles.

12. Apparatus for practicing the method of claim 1.

14. The apparatus of claim 13 wherein processor (a) includes:

(a1) a means for identifying the present value of the Kalman delta-phase component with a future value of the selected candidate delta-phase component.

15. The apparatus of claim 13 wherein processor (a) includes:

(a1) a means for obtaining a plurality of candidate error-state vectors by performing a second set of more than one Kalman filter processes, a candidate error-state vector being produced as an output of each second-set Kalman filter process utilizing a plurality of prior pseudorange values and the present position vector;

(a2) a means for selecting the error-state vector from the candidate error-state vectors by applying a predetermined error-state vector selection rule;

(a3) a means for determining the position vector using IMa outputs and the error-state vector.

16. The apparatus of claim 15 wherein each Kalman filter process in the second set is tuned to recognize either (1) the absence of any failure conditions or (2) the presence of a particular failure condition, a failure condition being the failure of a group of one or more functional elements of the inertial-GPS navigation system to perform within specifications.

17. The apparatus of claim 15 wherein the selected error-state vector is the error-state vector produced by the Kalman filter process producing a value of a failure-condition quantity that is statistically distinguishable from the values of the failure-condition quantity produced by the other second-set Kalman filter processes.

18. The apparatus of claim 17 whereby the failure-condition quantity is derived from weighted residuals summed over a plurality of Kalman filter process update cycles.

19. The apparatus of claim 13 wherein processor (b) includes:

(b1) a means for estimating future values of IMU acceleration and angular velocity from the IMU outputs;

(b2) a means for translating the future values of IMU acceleration and angular velocity into the IMU delta-phase component utilizing the direction-cosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertial-GPS navigation system.

20. The apparatus of claim 19 wherein processor (b) includes:

(b2-1) a means for obtaining a plurality of candidate error-state vectors by performing a second set of more than one Kalman filter processes, a candidate error-state vector being produced as an output of each second-set Kalman filter process utilizing a plurality of prior pseudorange values and the present position vector;

(b2-2) a means for selecting the error-state vector from the candidate error-state vectors by applying a predetermined error-state vector selection rule;

(b2-3) a means for determining the direction-cosine matrix using IMU outputs and the error-state vector.

21. The apparatus of claim 20 wherein each Kalman filter process in the second set is tuned to recognize either (1) the absence of any failure conditions or (2) the presence of a particular failure condition, a failure condition being the failure of a group of one or more functional elements of the inertial-GPS navigation system to perform within specifications.

22. The apparatus of claim 20 wherein the selected error-state vector is the error-state vector produced by the Kalman filter process producing a value of a failure-condition quantity that is statistically distinguishable from the values of the failure-condition quantity produced by the other second-set Kalman filter processes.

23. The apparatus of claim 22 wherein the failure-condition quantity is derived from weighted residuals summed over a plurality of Kalman filter process update cycles.

13. Apparatus for adjusting the phase and frequency of a received GPS signal in an inertial-GPS navigation system comprising an inertial measurement unit (IMU) and a GPS receiver, the GPS receiver having an input port for inputting a delta-phase at delta-time intervals into a received satellite signal, the GPS receiver having an output port for outputting a carrier phase error, the carrier phase error being the difference between the actual phase of the received GPS signal and a reference value, the GPS receiver having an output port for outputting a pseudorange associated with the received GPS signal, the IMW having an output port for outputting acceleration and angular velocity of the IMU, the delta-phase being the sum of at least two delta-phase components, the apparatus comprising:

(a) a processor for determining a Kalman delta-phase component, a plurality of candidate Kalman delta-phase components being obtained by performing a first set of more than one Kalman filter processes, the inputs to each first-set Kalman filter process including the carrier phase error and a position vector associated with the IMU, a candidate Kalman delta-phase component being produced as an output of each first-set Kalman filter process, the Kalman delta-phase component being determined from a candidate Kalman delta-phase components selected from the plurality of candidate Kalman delta-phase components by applying a predetermined Kalman delta-phase component selection rule;

(b) a processor for determining an IMU delta-phase component from the IMU outputs and a direction-cosine matrix that translates the body coordinates of the IMU into the navigation coordinates of the inertial-GPS navigation system.