Systems and methods to incorporate master navigation system resets during transfer alignment

• US 8,515,672 B1
• Filed: 03/01/2012
• Issued: 08/20/2013
• Est. Priority Date: 03/01/2012
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Snyder, Scott, Mohr, Benjamin
• Primary Examiner(s): Tarcza, Thomas
• Assistant Examiner(s): ALHARBI, ADAM MOHAMED
• Application Number:
  US13/409,707
• Publication Number:
  US 20130231860A1
• Time in Patent Office: 537 Days
• Field of Search: None
• US Class Current: 701/510
• CPC Class Codes: F41G 7/007 Preparatory measures taken before the launching of the guided missiles
• CPC Class Codes: F41G 7/36 using inertial references
• CPC Class Codes: G01C 21/188 for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
• CPC Class Codes: G01C 25/00 Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass testing, calibrating or compensating compasses G01C17/38

Abstract

Systems and methods to incorporate master navigation system resets during transfer alignment are provided. In one embodiment, a system comprises: a set of local inertial sensors; a local navigation processor coupled to local inertial sensors, the local navigation processor receiving inertial navigation data from local inertial sensors and converting the data into a navigation solution; a local Kalman filter (LKF) coupled to the local navigation processor and a master Kalman filter (MKF), the LKF receiving the navigation solution. The LKF receives from the MKF a Precision Transfer Alignment Message (PTAM) that includes at least one navigation aid measurement. The LKF inputs the navigation aid measurement into a measurement formation algorithm and calculates a measurement residual. The LKF receives from the MKF a Reset Transfer Alignment Message (RTAM) that includes a bias correction. The LKF inputs the bias correction into a state propagation algorithm to add to a navigation state.

Claims

1. A navigation system for performing a transfer alignment, the navigation system comprising:

a set of local inertial sensors;

a local strap-down navigation processor coupled to the set of local inertial sensors, the local strap-down navigation processor receiving inertial navigation data from the set of local inertial sensors and converting the inertial navigation data into a navigation solution;

a local Kalman filter coupled to the local strap-down navigation processor, the local Kalman filter receiving the navigation solution produced by the local strap-down navigation processor, the local Kalman filter further coupled to a master Kalman filter;

wherein the local Kalman filter receives a first message from the master Kalman filter comprising a Precision Transfer Alignment Message (PTAM) that includes at least one navigation aid measurement;

wherein the local Kalman filter inputs the navigation aid measurement into a measurement formation algorithm and calculates a measurement residual based on the navigation aid measurement;

wherein the local Kalman filter receives a second message from the master Kalman filter comprising a Reset Transfer Alignment Message (RTAM) that includes a bias correction applied by the master Kalman filter as a navigation reset;

wherein the local Kalman filter inputs the bias correction into a state propagation algorithm where the bias correction provided by the bias correction is added to a navigation state of the local Kalman filter and propagated.

2. The navigation system of claim 1, wherein the bias correction provided by the RTAM includes one of a position correction, a velocity correction, an attitude correction, or a combination thereof.

3. The navigation system of claim 1, wherein the bias correction comprises a nine-dimensional vector comprising a 3-dimensional position bias correction, a 3-dimensional velocity correction, and a 3-dimensional attitude correction.

4. The navigation system of claim 1, wherein the PTAM communicates the at least one navigation aid measurement as absolute navigation solution values;

and

wherein the RTAM communications the bias correction as a change in navigation solution values.

5. The navigation system of claim 1, wherein the set of local inertial sensors comprises at least a triad of orthogonal accelerometers and a triad of orthogonal gyroscopes.

6. The navigation system of claim 1, wherein the local Kalman filter is implemented using an Extended Kalman Filter algorithm.

7. The navigation system of claim 1, further comprising a processing element;

wherein the local Kalman filter is implemented as an algorithm executed by the processing element.

8. The navigation system of claim 1, wherein the local strap-down navigation processor and the local Kalman filter are both implemented using the same processor.

9. The navigation system of claim 1, wherein the measurement formation algorithm is calculated using an equation equivalent to:

y_k=PTAM_n−

h(x_k^÷

).

10. The navigation system of claim 1, wherein the state propagation algorithm is calculated using an equation equivalent to:

Δ

x_k⁻

=φ

_kΔ

x<sub>k−

1</sub>^÷

−

r_k÷

T_k

where T_k is the bias correction provided by the RTAM.

20. The navigation system of claim 1, wherein the measurement formation algorithm is calculated using an equation equivalent to:

y_k=PTAM_n−

h(x_k^÷

)

and wherein the state propagation algorithm is calculated using an equation equivalent to;

Δ

x_k⁻

=φ

_kΔ

x<sub>k−

1</sub>^÷

−

r_k÷

T_k

where T_k is the bias correction provided by the RTAM.

11. A method for performing a transfer alignment from a host navigation system to a payload navigation system, the method comprising:

receiving a navigation solution from a local strap down navigation processor;

receiving a Precision Transfer Alignment Message (PTAM) at a local Kalman filter, the PTAM comprising at least one navigation aid measurement generated by a master Kalman filter;

receiving a Reset Transfer Alignment Message (RTAM) at a local Kalman filter, the RTAM comprising a bias correction applied by the master Kalman filter as a navigation reset;

propagating a navigation state at the local Kalman filter using the navigation solution from a local strap down navigation processor, wherein the local Kalman filter adds the bias correction to the navigation state;

computing a measurement update at the local Kalman filter as a function of the at least one navigation aid measurement and the bias correction, wherein the navigation solution from a local strap down navigation processor is compensated for the bias correction by the measurement update.

12. The method of claim 11, wherein the bias correction provided by the RTAM includes one of a position correction, a velocity correction, an attitude correction, or a combination thereof.

13. The method of claim 11, wherein the bias correction comprises a nine-dimensional vector comprising a 3-dimensional position bias correction, a 3-dimensional velocity correction, and a 3-dimensional attitude correction.

14. The method of claim 11, wherein the PTAM communicates the at least one navigation aid measurement as absolute navigation solution values;

and

wherein the RTAM communications the bias correction as a change in navigation solution values.

15. The method of claim 11, wherein the local strap down navigation processor is coupled to a set of local inertial sensors that comprises at least a triad of orthogonal accelerometers and a triad of orthogonal gyroscopes.

16. The method of claim 11, wherein the local Kalman filter is implemented using an Extended Kalman Filter algorithm.

17. The method of claim 11, wherein propagating the navigation state at the local Kalman filter is calculated using an equation equivalent to:

Δ

x_k⁻

=φ

_kΔ

x<sub>k−

1</sub>^÷

−

r_k÷

T_k

where T_k is the bias correction provided by the RTAM.

18. The method of claim 17, wherein computing a measurement update at the local Kalman filter is calculated using an equation equivalent to:

Δ

x_k^÷

=Δ

x_k⁻

÷

K_k(y_k−

H_kΔ

x_k⁻

)

where

y_k=PTAM_n−

h(x_k^÷

).

19. A system comprising:

an aircraft having a aircraft navigation system comprising a master Kalman filter;

a payload loaded onto the aircraft, the payload having a payload navigation system comprising;

a set of local inertial sensors;

a local strap-down navigation processor coupled to the set of local inertial sensors, the strap-down navigation processor, the local strap-down navigation processor receiving inertial navigation data from the set of local inertial sensors and converting the inertial navigation data into a navigation solution;

a local Kalman filter coupled to the local strap-down navigation processor, the local Kalman filter receiving the navigation solution produced by the local strap-down navigation processor, the local Kalman filter further coupled to the master Kalman filter;

wherein the local Kalman filter receives a first message from the master Kalman filter comprising a Precision Transfer Alignment Message (PTAM) that includes at least one navigation aid measurement;

wherein the local Kalman filter inputs the navigation aid measurement into a measurement formation algorithm and calculates a measurement residual based on the navigation aid measurement;

wherein the local Kalman filter receives a second message from the master Kalman filter comprising a Reset Transfer Alignment Message (RTAM) that includes a bias correction applied by the master Kalman filter as a navigation reset;

wherein the local Kalman filter inputs the bias correction into a state propagation algorithm where the bias correction provided by the bias correction is added to a navigation state of the local Kalman filter and propagated.