Method of determining a collision avoidance maneuver

1. A method of determining a collision avoidance maneuver, comprising:

obtaining initial state data including initial state data for a first object and a second object;

generating a plurality of preliminary maneuvers satisfying a first set of constraints using the initial state data;

selecting a best preliminary maneuver from the plurality of preliminary maneuvers; and

optimizing the best preliminary maneuver according to an objective function to provide a final maneuver, the optimization adhering to a provided second set of constraints.

2. The method of claim 1 , wherein each preliminary maneuver has parameters further comprising an epoch and a delta velocity vector.

3. The method of claim 2 , wherein selecting a best preliminary maneuver includes selecting a preliminary maneuver having the smallest delta velocity vector magnitude.

4. The method of claim 1 , wherein each preliminary maneuver has parameters further comprising a start epoch, a thrust direction, and a burn duration.

5. The method of claim 4 , wherein selecting a best preliminary maneuver includes selecting a preliminary maneuver having a shortest burn duration.

6. The method of claim 1 , wherein the final maneuver has parameters further comprising an epoch and a delta velocity vector.

7. The method of claim 6 , wherein the objective is to minimize a magnitude of the delta velocity vector.

8. The method of claim 1 , wherein the final maneuver has parameters further comprising a start epoch, a thrust direction, and a burn duration.

9. The method of claim 8 , wherein the objective is to minimize the burn duration.

10. The method of claim 1 , wherein optimizing the best preliminary maneuver includes using a numerical optimizer subsystem.

11. The method of claim 1 , wherein the initial state data further comprises:

a first state vector associated with the first object and a second state vector associated with the second object; and

a first covariance matrix associated with a position of the first object and a second covariance matrix associated with a position of the second object.

12. The method of claim 1 , wherein the first set of constraints further comprises a first minimum miss distance constraint and a first probability of collision constraint.

13. The method of claim 12 , wherein the first probability is a nominal probability determined by evaluating a relative covariance as a constant formed from a component covariance of the first object and a component covariance of the second object.

14. The method of claim 12 , wherein the first probability is a maximum probability determined by evaluating a relative covariance along one dimension over a defined interval to scale against a component covariance of the first object and a component covariance of the second object.

15. The method of claim 12 , wherein the first probability is a maximum probability determined by evaluating a relative covariance as a function of two variables, a first variable as a scale against a component covariance of the first object and a second variable as a scale against a component covariance of the second object.

16. The method of claim 1 , wherein the second set of constraints further comprises a second minimum miss distance constraint, a second probability of collision constraint, and a maneuver time window.

17. The method of claim 16 , wherein the second probability is a nominal probability determined by evaluating a relative covariance as a constant formed from a component covariance of the first object and a component covariance of the second object.

18. The method of claim 16 , wherein the second probability is a maximum probability determined by evaluating a relative covariance along one dimension over a defined interval to scale against a component covariance of the first object and a component covariance of the second object.

19. The method of claim 16 , wherein the second probability is a maximum probability determined by evaluating a relative covariance as a function of two variables, a first variable as a scale against a component covariance of the first object and a second variable as a scale against a component covariance of the second object.

20. The method of claim 16 , wherein the second set of constraints further comprises a relative dot product value and a requirement that a difference consisting of a final maneuver execution time minus a time of closest approach be a positive value.

21. The method of claim 1 , wherein the objective function specifies an optimizing objective.

22. The method of claim 1 , wherein the objective function relates the second set of constraints to the final maneuver.

23. The method of claim 1 further comprising:

analyzing a conjunction to determine a minimum miss distance and a probability of collision; and

determining the collision avoidance maneuver only if the minimum miss distance is less than a first threshold or if the probability of collision exceeds a second threshold.

24. A method of determining a collision avoidance maneuver, comprising:

obtaining initial state data including position data, position covariance data, and velocity data for a first object and a second object;

determining a plurality of preliminary maneuvers, including the steps of:

determining a plurality of points, each point having an unique combination of maneuver parameters including an epoch value, a yaw value, a pitch value, and a general maneuver direction;

creating a preliminary maneuver from each point, each preliminary maneuver initially having a predetermined minimum delta velocity vector magnitude;

simulating each preliminary maneuver and evaluating parameters constrained by a first set of constraints; and

increasing the delta velocity vector magnitude of each preliminary maneuver in discrete steps until the preliminary maneuver adheres to the first set of constraints;

designating a preliminary maneuver having a smallest delta velocity vector magnitude as a best preliminary maneuver; and

optimizing the best preliminary maneuver according to an objective function to provide a final maneuver using a numerical optimizer subsystem, the final maneuver having about a smallest delta velocity vector magnitude allowing the final maneuver to adhere to a second set of constraints.

25. The method of claim 24 , wherein the first set of constraints includes a first probability of collision determined;

in a first instance as a nominal probability determined by evaluating a relative covariance as a constant formed from a component covariance of the first object and a component covariance of the second object;

in a second instance as a maximum probability determined by evaluating a relative covariance along one dimension over a defined interval to scale against a component covariance of the first object and a component covariance of the second object; and

in a third instance as a maximum probability determined by evaluating a relative covariance as a function of two variables, a first variable as a scale against a component covariance of the first object and a second variable as a scale against a component covariance of the second object; and

the second set of constraints includes a second probability of collision determined;

in a first instance as a nominal probability determined by evaluating a relative covariance as a constant formed from a component covariance of the first object and a component covariance of the second object;

in a second instance as a maximum probability determined by evaluating a relative covariance along one dimension over a defined interval to scale against a component covariance of the first object and a component covariance of the second object; and

in a third instance as a maximum probability determined by evaluating a relative covariance as a function of two variables, a first variable as a scale against a component covariance of the first object and a second variable as a scale against a component covariance of the second object.

26. A method of determining a collision avoidance maneuver, comprising:

obtaining initial state data including position data, position covariance data, and velocity data for a first object and a second object;

determining a plurality of preliminary maneuvers, including the steps of:

determining a plurality of points, each point having an unique combination of maneuver parameters including a start epoch value, a yaw value, a pitch value, and a general maneuver direction;

creating a preliminary maneuver from each point, each preliminary maneuver initially having a predetermined minimum burn duration;

simulating each preliminary maneuver and evaluating parameters constrained by a first set of constraints; and

increasing the burn duration of each preliminary maneuver in discrete steps until the preliminary maneuver adheres to the first set of constraints;

designating a preliminary maneuver having a shortest burn duration as a best preliminary maneuver; and

optimizing the best preliminary maneuver according to an objective function to provide a final maneuver using a numerical optimizer subsystem, the final maneuver having about a shortest burn duration allowing the final maneuver to adhere to a second set of constraints.

27. The method of claim 26 , wherein the first set of constraints includes a first probability of collision determined;

in a first instance as a nominal probability determined by evaluating a relative covariance as a constant formed from a component covariance of the first object and a component covariance of the second object;

in a second instance as a maximum probability determined by evaluating a relative covariance along one dimension over a defined interval to scale against a component covariance of the first object and a component covariance of the second object; and

in a third instance as a maximum probability determined by evaluating a relative covariance as a function of two variables, a first variable as a scale against a component covariance of the first object and a second variable as a scale against a component covariance of the second object; and

the second set of constraints includes a second probability of collision determined;

in a first instance as a nominal probability determined by evaluating a relative covariance as a constant formed from a component covariance of the first object and a component covariance of the second object;

in a second instance as a maximum probability determined by evaluating a relative covariance along one dimension over a defined interval to scale against a component covariance of the first object and a component covariance of the second object; and

in a third instance as a maximum probability determined by evaluating a relative covariance as a function of two variables, a first variable as a scale against a component covariance of the first object and a second variable as a scale against a component covariance of the second object.

28. A computer system for determining a collision avoidance maneuver, comprising:

a processing unit;

a memory storage device coupled to the processing unit;

an input device coupled to the processing unit;

an output device coupled to the processing unit;

the processing unit being operative to:

obtain initial state data including initial state data for a first object and a second object;

generate a plurality of preliminary maneuvers satisfying a first set of constraints using the initial state data;

select a best preliminary maneuver from the plurality of preliminary maneuvers; and

optimize the best preliminary maneuver according to an objective function to provide a final maneuver, the optimization adhering to a provided second set of constraints.

29. A software product comprising instructions, stored on computer-readable media, wherein the instructions, when executed by a computer, perform steps for determining a collision avoidance maneuver, comprising:

an input routine operatively associated with an input device for obtaining initial state data including initial state data for a first object and a second object;

a preliminary maneuver generating routine for generating a plurality of preliminary maneuvers satisfying a first set of constraints using the initial state data;

a selection routine for selecting a best preliminary maneuver from the plurality of preliminary maneuvers; and

an optimizing routine for optimizing the best preliminary maneuver according to an objective function to provide a final maneuver, the optimization routine adhering to a provided second constraint.

30. The software product of claim 21 , wherein multiple instantiations of the routines execute substantially concurrently.