Extended kalman filter for autonomous satellite navigation system

• US 20030132878A1
• Filed: 12/30/2002
• Published: 07/17/2003
• Est. Priority Date: 04/21/1999
• Status: Active Grant

• Current Assignee: Albert A. Chacos, Dennis J. Duven, Don M. Gruenbacher, Kim Strohbehn, Lloyd A. Linstrom, Mark S. Asher, Richard C. Morgan, Robert J. Heins, Thomas L. Kusterer, William S. Devereux
• Original Assignee: Albert A. Chacos, Dennis J. Duven, Don M. Gruenbacher, Kim Strohbehn, Lloyd A. Linstrom, Mark S. Asher, Richard C. Morgan, Robert J. Heins, Thomas L. Kusterer, William S. Devereux
• Inventors: Gruenbacher, Don M., Heins, Robert J., Asher, Mark S., Kusterer, Thomas L., Linstrom, Lloyd A., Duven, Dennis J., Chacos, Albert A., Morgan, Richard C., Devereux, William S., Strohbehn, Kim
• Granted Patent: US 6,859,170 B2
• Time in Patent Office: Days
• US Class Current: 342/357.06
• CPC Class Codes: G01S 19/14 specially adapted for specific applications
• CPC Class Codes: G01S 19/37 Hardware or software details of the signal processing chain
• CPC Class Codes: G01S 19/54 using carrier phase measurements; using long or short baseline interferometry

Abstract

An autonomous navigation system for an orbital platform incorporating a global positioning system based navigation device optimized for low-Earth orbit and medium-Earth orbit applications including a 12 channel, GPS tracking application-specific integrated circuit (15) operating in concert with a computer system (90) implementing an extended Kalman filter and orbit propagator which autonomously generates estimates of position, velocity and time to enable planning, prediction and execution of event-based commanding of mission operations.

Claims

1. A method for orbital platform navigation, including the steps of:

sensing the data transmitted by a plurality of global positioning system satellites;

determining from said sensed transmitted data through the application of a software implemented Kalman filter, data defining current time and position and velocity of said orbital platform; and

initiating and controlling the operation of systems on-board said orbital platform based on said data defining current time and position and velocity.

2. A method for orbital platform navigation as defined by claim 1, wherein said Kalman filter utilizes previously determined data defining time and position and velocity when performing said step of determining from said sensed transmitted data, current time and position and velocity.

3. A method for orbital platform navigation as defined by claim 2, wherein said step of determining current time and position and velocity includes steps executed by said Kalman filter of calculating a square-root covariance form.

4. A method for orbital platform navigation as defined by claim 3, wherein said Kalman filter performs the steps of determining the measurement of slant range from said orbital platform to each of said global positioning system satellites providing data used in determining said data defining current time and position and velocity.

5. A method for orbital platform navigation as defined by claim 4, wherein said plurality of global positioning system satellites comprise at least three satellites.

6. A method for determining orbital platform navigation as defined by claim 5, wherein said Kalman filter performs the steps of providing a plurality of state space determinations arranged in first and second partitions;

said first partition including states which directly determined the orbital platform position, velocity, drag parameter error, receiver clock and frequency bias; and

said second partition includes states not directly affecting position and time.

7. A method for orbital platform navigation as defined by claim 6, wherein said steps of providing a plurality of state space determinations include the steps of:

calculating a position state space;

calculating a velocity state space;

calculating a drag parameter error state space;

calculating a clock error state space;

calculating a frequency error state space;

calculating a selective availability and Markov process state space; and

calculating an integrated carrier phase bias state space.

8. A method for orbital platform navigation as defined by claim 6, wherein said steps of providing a plurality of state space determinations include the steps of:

calculating three position state spaces;

calculating three velocity state spaces;

calculating a drag parameter error state space;

calculating a clock error state space;

calculating a frequency error state space;

calculating 24 selected availability and Markov process state spaces; and

calculating 12 integrated carrier phase bias state spaces.

9. A method for orbital platform navigation as defined by claim 6, wherein said steps of providing a plurality of state space determinations include the steps of:

calculating a plurality of dynamic states including, a position state space, a velocity state space, a drag parameter error state space, a clock error state space, and a frequency error state space; and

dynamically coupling said dynamic states to each other.

10. A method for orbital platform navigation as defined by claim 3, wherein said Kalman filter performs said step of updating said data defining current time by executing steps propagating the state and covariance between measurements.

11. A method for orbital platform navigation as defined by claim 9, wherein said step of propagating the state uses time as an independent variable.

12. A method for orbital platform navigation as defined by claim 3, wherein said steps of determining current time and position and velocity include update steps executed by said Kalman filter comprised of propagating state and covariance from the present to the next anticipated measurement for the dynamic states of position, velocity, drag, and clock, each of which is expressed as a function {dot over (x)}_dyn=f(x_dyn,t) where t equals time, according to the nonlinear function

13. A method for orbital platform navigation as defined by claim 12, wherein the deterministic part of said covariance factor F used in performing said update steps is defined by

14. A method for orbital platform navigation as defined by claim 13, including the Kalman filter executed steps of propagating state and covariance from the present to the next anticipated measurement for the states and biases which obey linear dynamics according to the function

Φ

_pdr(t_k+1,t_k)=S1(t_k+1,t_k).

15. A method for orbital platform navigation as defined by claim 14, wherein said states and biases which obey linear dynamics include availability states for said global positioning system satellites and phase biases.