SELECTED ASPECTS OF ADVANCED RECEIVER AUTONOMOUS INTEGRITY MONITORING APPLICATION TO KALMAN FILTER BASED NAVIGATION FILTER

• US 20140292574A1
• Filed: 03/26/2013
• Published: 10/02/2014
• Est. Priority Date: 03/26/2013
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Dunik, Jindrich, Orejas, Martin, Kana, Zdenek
• Granted Patent: US 9,547,086 B2
• Time in Patent Office: Days
• US Class Current: 342/357.58
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01S 19/20 Integrity monitoring, fault detection or fault isolation of space segment
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

A method of advanced receiver autonomous integrity monitoring of a navigation system is discussed and two modifications facilitating its implementation in a hybrid navigation system are disclosed. In the first approach, relations describing the effect of unmodeled biases in pseudo-measurement on the Kalman filter state estimate are analytically derived and their incorporation into the integrity monitoring algorithm is described. The method comprises receiving a plurality of signals transmitted from spaced-based satellites, determining a position full-solution and sub-solutions, specifying a pseudorange bias, computing a transformation matrix for the full-solution and all sub-solutions using a Kalman filter, computing a bias effect on an error of filtered state vectors of all sub-solutions, and adding the effect to computed vertical and horizontal protection levels. In the second approach, a modification for computationally effective calculation of the protection levels of hybrid navigation systems based on both integrity and non-integrity assured pseudorange error descriptions is disclosed.

Claims

1. A method of integrity monitoring for errors in a navigation system, the method comprising:

receiving a plurality of signals transmitted from a plurality of spaced-based satellites;

determining a full position solution for the navigation system based on the plurality of signals;

determining one or more position sub-solutions for the navigation system based on the plurality of signals;

setting a value for a pseudorange bias;

computing a transformation matrix for the full solution and all sub-solutions using a Kalman filter;

computing a bias effect on an error of a filtered state vector component for all sub-solutions; and

adding the bias effect on the error of the filtered state vector component to computed protection levels for both vertical and horizontal dimensions.

2. The method of claim 1, wherein the state vector component comprises position, velocity, or attitude.

3. The method of claim 1, wherein the method is implemented as part of an advanced receiver autonomous integrity monitoring algorithm.

4. The method of claim 1, wherein the spaced-based satellites are part of a global navigation satellite system (GNSS).

5. The method of claim 4, wherein the GNSS is a multi-constellation, multi-frequency GNSS.

6. A method of integrity monitoring for errors in a navigation system, the method comprising:

receiving a plurality of signals transmitted from a plurality of spaced-based satellites;

determining a full position solution for the navigation system based on the plurality of signals;

determining one or more position sub-solutions for the navigation system based on the plurality of signals;

computing a variance of a vertical position for the full position solution with a state estimation filter based on an integrity assured error definition;

computing a variance of a vertical position for the full position solution with the state estimation filter based on a non-integrity assured error definition;

computing a variance of the vertical position for all position sub-solutions with one or more additional state estimation filters based on the integrity assured error definition;

computing a multiplying coefficient; and

computing an approximate variance of the vertical position for all position sub-solutions based on the non-integrity assured error definition and the multiplying coefficient.

7. The method of claim 6, further comprising:

computing a covariance matrix of the horizontal position for a full position solution with the state estimation filter based on the integrity assured error definition;

computing a covariance matrix of the horizontal position for the full position solution with the state estimation filter based on the non-integrity assured error definition;

computing a covariance matrix of the horizontal position for all position sub-solutions with the one or more additional state estimation filters based on the integrity assured error definition;

computing maximum eigenvalues for the horizontal position covariance matrices;

computing a multiplying coefficient; and

computing approximate maximum eigenvalues for the horizontal position covariance matrices for all position sub-solutions based on the non-integrity assured error definition and the multiplying coefficient.

8. The method of claim 6, wherein the state estimation filter comprises a Kalman filter, an extended Kalman filter, an unscented Kalman filter, a particle filter, or a Gaussian sum filter.

9. The method of claim 7, wherein the method is implemented as part of an advanced receiver autonomous integrity monitoring (ARAIM) algorithm.

10. The method of claim 6, wherein the spaced-based satellites are part of a global navigation satellite system (GNSS).

11. The method of claim 10, wherein the GNSS is a multi-constellation, multi-frequency GNSS.

12. A navigation system, comprising:

one or more inertial sensors that generate inertial measurements;

a receiver configured to receive a plurality of signals transmitted from a plurality of spaced-based satellites; and

a processor operatively coupled to the receiver and the inertial sensors, the processor configured to perform a method of integrity monitoring implemented as part of an advanced receiver autonomous integrity monitoring algorithm.

13. The navigation system of claim 12, wherein the integrity monitoring performed by the processor comprises:

determining a full position solution for the navigation system based on the plurality of signals;

determining one or more position sub-solutions for the navigation system based on the plurality of signals;

setting a value for a pseudorange bias;

computing a transformation matrix for the full solution and all sub-solutions using a Kalman filter;

computing a bias effect on an error of a filtered state vector component for all sub-solutions; and

adding the bias effect on the error of the filtered state vector component to computed protection levels for both vertical and horizontal dimensions.

14. The navigation system of claim 12, wherein the integrity monitoring performed by the processor comprises:

determining a full position solution for the navigation system based on the plurality of signals;

determining one or more position sub-solutions for the navigation system based on the plurality of signals;

computing a variance of a vertical position for the full position solution with a Kalman filter based on an integrity assured error definition;

computing a variance of a vertical position for the full position solution with the Kalman filter based on a non-integrity assured error definition;

computing a variance of the vertical position for all position sub-solutions with one or more additional Kalman filters based on the integrity assured error definition;

computing a multiplying coefficient; and

computing an approximate variance of the vertical position for all position sub-solutions based on the non-integrity assured error definitions and the multiplying coefficient.

15. The navigation system of claim 14, wherein the integrity monitoring performed by the processor further comprises:

computing a covariance matrix of the horizontal position for a full position solution with the Kalman filter based on the integrity assured error definition;

computing a covariance matrix of the horizontal position for the full position solution with the Kalman filter based on the non-integrity assured error definition;

computing a covariance matrix of the horizontal position for all position sub-solutions with the one or more additional Kalman filters based on the integrity assured error definition;

computing maximum eigenvalues for the horizontal position covariance matrices;

computing a multiplying coefficient; and

computing approximate maximum eigenvalues for the horizontal position covariance matrices for all position sub-solutions based on the non-integrity assured error definition and the multiplying coefficient.

16. The system of claim 12, wherein the receiver is configured to receive signals from a global navigation satellite system (GNSS).

17. The system of claim 12, wherein the receiver is configured to receive signals from a multi-constellation, multi-frequency GNSS.

18. The system of claim 12, wherein the inertial sensors are implemented in an inertial measurement unit.

19. The system of claim 12, further comprising one or more additional sensors in operative communication with the processor.

20. The system of claim 12, wherein the system is onboard an aircraft.