Multipath Modeling For Deep Integration

• US 20080082266A1
• Filed: 09/29/2006
• Published: 04/03/2008
• Est. Priority Date: 09/29/2006
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Bye, Charles T., Schipper, Brian W.
• Granted Patent: US 8,600,660 B2
• Time in Patent Office: Days
• US Class Current: 701/214
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G01S 19/22 Multipath-related issues
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
• CPC Class Codes: H03H 17/0257 KALMAN filters

Abstract

A state is added to a Kalman filter to model GPS multipath errors. The multipath states may be modeled as either a random walk model or a Gauss-Markov process. The choice of the model depends on the characteristics of the multi-path error and the GPS receiver. Adding this state to the Kalman filter to model multipath improves the navigation system'"'"'s robustness when operating as a deeply integrated system when multipath is present.

Claims

1. A deeply integrated navigation system that operates in the presence of multipath, comprising in combination:

a navigation processor that receives data from inertial sensors and computes a navigation solution; and

a Kalman filter that includes a multipath error model, wherein the Kalman filter receives the navigation solution and data from a global positioning satellite (GPS) receiver, and uses the navigation solution, the data from the GPS receiver, and the multipath error model to calculate an estimate that is provided to the navigation processor.

2. The system of claim 1, wherein the multipath error model is a random walk model.

3. The system of claim 1, wherein the multipath error model is a Gauss-Markov process.

4. A method for improving deep integration performance of a navigation system in the presence of multipath, comprising in combination:

receiving data from inertial sensors and a global positioning satellite (GPS) receiver;

modeling a multipath state in a Kalman filter; and

calculating a navigation solution based on the received data and a navigation estimate that is calculated using the modeled multipath state.

5. The method of claim 4, wherein modeling the multipath state includes using a random walk model.

6. The method of claim 4, wherein modeling the multipath state includes using a Gauss-Markov process.

7. A method for improving deep integration performance of a navigation system in the presence of multipath, comprising in combination:

receiving data from inertial sensors and a global positioning satellite (GPS) receiver;

calculating a Kalman gain using a first error covariance matrix that includes a GPS short term error state;

calculating a first estimate with the received data, the Kalman gain, and a state vector that includes the GPS short term error state; and

calculating a second estimate and a second error covariance matrix using a state transition matrix that includes the GPS short term error state.

8. The method of claim 7, wherein the Kalman gain K is calculated as follows:

K_n=P_n⁻

H_n^T(H_nP_n⁻

H_n^T+R_n)<sup>−

1</sup>

where P is the error covariance matrix, H is a measurement matrix, and R is a measurement noise covariance matrix.

9. The method of claim 7, wherein the GPS short term error state includes multipath.

10. The method of claim 7, wherein the GPS short term error state is modeled as a random walk model.

11. The method of claim 7, wherein the GPS short term error state is modeled as a Gauss-Markov process.

12. The method of claim 7, wherein the first estimate Xn is calculated as follows:

{circumflex over (x)}_n={circumflex over (x)}_n⁻

+K_n(Z_n−

H_n{circumflex over (x)}_n⁻

)

where K is the Kalman gain, Z is a measurement vector, and H is a measurement matrix.

13. The method of claim 7, wherein the first error covariance matrix P is calculated as follows:

P_n=(1−

K_nH_n)P_n⁻

where K is the Kalman gain and H is a measurement matrix.

14. The method of claim 7, wherein the second estimate {circumflex over (x)}_n is calculated as follows:

{circumflex over (x)}_n+1⁻

=Φ

_n{circumflex over (x)}_n

where Φ

is the state transition matrix.

15. The method of claim 7, wherein the second error covariance matrix P is calculated as follows:

P_n+1⁻

=Φ

_nP_nΦ

_n^T+Q_n

where Φ

is the state transition matrix and Q is the process noise covariance matrix.