Sensor scan planner

• US 7,676,064 B2
• Filed: 05/17/2006
• Issued: 03/09/2010
• Est. Priority Date: 05/17/2006
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Lee, Chang-Shaung R., Neff, Michael G., Cheng, Shirley N., Johnson, Ted L.
• Primary Examiner(s): Lu; Tom Y
• Application Number:
  US11/383,900
• Publication Number:
  US 20070269077A1
• Time in Patent Office: 1,392 Days
• Field of Search: 382/103, 382/104, 348/169, 701/202
• US Class Current: 382/103
• CPC Class Codes: F41H 13/00 Means of attack or defence not otherwise provided for details of radar or sonar systems G01S7/00
• CPC Class Codes: G01S 7/41 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G05D 1/107 specially adapted for missiles
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

Sensor scan planner methods and systems are described. In an embodiment, a sensor scan schedule can be generated by a sensor scan schedule generator to optimize the scan schedules of multiple sensors based on optimal capabilities of each sensor and autonomous target recognition algorithm processing. A search manager can then assign an evaluation criteria value to the generated sensor scan schedule based on sensor scan schedule evaluation criteria, and compare the evaluation criteria value to other evaluation criteria values corresponding to respective previously generated sensor scan schedules to determine an optimal sensor scan schedule. The search manager can then determine whether to generate additional sensor scan schedules and assign additional evaluation criteria values for comparison to determine the optimal sensor scan schedule.

Claims

1. A method for a vehicle utilizing multiple onboard sensors during a mission, the method comprising using a computer to:

generate a plurality of sensor scan schedules that a vehicle could utilize during the mission, each sensor scan schedule correlating scans of the multiple sensors to different autonomous target recognition algorithm processors;

compare each generated sensor scan schedule to evaluation criteria to determine a contribution towards mission accomplishment that would result from using the that scan schedule along the vehicle'"'"'s velocity vector each whether the generated schedule is optimal, the evaluation criteria value corresponding to respective previously generated sensor scan schedules; and

use the comparisons to determine a sensor scan schedule that results in a larger contribution toward mission accomplishment.

2. A method as recited in claim 1, wherein each comparison includes determining whether the generated sensor scan schedule meets a conditional probability threshold based on the evaluation criteria.

3. A method as recited in claim 2, wherein the evaluation criteria determines a contribution towards mission accomplishment that would also result from commit logic that indicates mission specific criteria which determine whether the vehicle can commit to a particular object including the likelihood the vehicle can reach an object, the likelihood an object is of a mission desired type, and the likelihood the object is the mission desired object.

4. A method as recited in claim 1, wherein accomplishing the mission includes searching, recognizing, and committing to targets in a region;

and wherein the evaluation criteria includes representations of targets in the region.

5. A method as recited in claim 1, wherein the evaluation criteria determines a contribution towards mission accomplishment that would also result from sensor and autonomous target recognition algorithm probability model processing where a sensor and autonomous target recognition algorithm probability model characterizes predicted object recognition performance and resource allocation for each combination of sensor mode and autonomous target recognition algorithm combination.

6. A method as recited in claim 1, further comprising generating a probability map from previous sensor scans combined with a projected target location of one or more relocatable targets in a target area, and wherein each sensor scan schedule is generated based at least in part on the probability map.

7. A method as recited in claim 6, further comprising:

receiving a targeting input as at least one of a sensor scan input;

a data link input;

generating estimated object states from the inputs;

determining a previously known target location of one or more relocatable targets from the targeting input;

projecting object states from the predicted object states and target locations; and

combining the projected object states with prior sensor scans to determine a conditional probability towards mission accomplishment.

17. The method of claim 1, wherein sensor and ATR performance model inputs are used to define optimal capabilities of each sensor and autonomous target recognition algorithm, wherein the sensor scan schedule generator uses these inputs to determine which combination of sensors and multiple processors for the autonomous target recognition algorithms, as well as which sequence of sensor scans, provide an optimization for the greatest likelihood of target detection.

18. The method of claim 1, wherein each sensor scan schedule of the plurality is assigned an evaluation criteria value;

and wherein the evaluation criteria value of the last-generated sensor scan schedule is compared to other evaluation criteria values of previously generated sensor scan schedules to determine an optimal sensor scan schedule.

19. The method of claim 18, wherein the evaluation criteria includes an ATR algorithm dependency factor which indicates the statistical dependency of ATR results produced from sensor scans of the same area which are close in time, have similar relative geometries, were produced by different sensors, or were produced by different ATR algorithms.

20. The method of claim 6, wherein information associated with a previous sensor scan includes a type of sensor, scan pattern, direction, resolution, and scan time, and a position of the vehicle as determined by an inertial guidance system.

8. A sensor scan planner system, comprising:

a sensor scan schedule generator for generating a plurality of sensor scan schedules that a vehicle can utilize during a mission, each scan schedule correlating scans of multiple sensors based with different algorithm processing;

sensor scan schedule evaluation criteria for determining a contribution towards mission accomplishment that would result from using each scan schedule along the vehicle'"'"'s velocity vector; and

a search manager for using the evaluation criteria to guide the schedule generator to produce the sensor scan schedule that results in a larger contribution toward mission accomplishment.

9. A sensor scan planner system as recited in claim 8, wherein the vehicle is an airborne platform, and wherein the sensor scan schedule generator generates the plurality of scan schedules along a current velocity vector of the airborne platform to search and locate at least one relocatable target.

10. A sensor scan planner system as recited in claim 8, wherein accomplishing the mission includes searching, recognizing, and committing to targets in a region;

and wherein the evaluation criteria includes representations of targets in the region.

11. A sensor scan planner system as recited in claim 10, further comprising a commit logic module containing a set of mission specific criteria which determine whether the vehicle can commit to a particular target, wherein the evaluation criteria determines a contribution towards mission accomplishment that would also result from the commit logic indicating whether to commit to the target.

12. A sensor scan planner system as recited in claim 8, further comprising:

a fusion track manager configured to receive sensor scan and data link inputs, and generate estimated object states from the inputs.

13. A sensor scan planner system as recited in claim 12, wherein the evaluation criteria characterizes information from new sensor scans based on the information already gained from previous sensor scans, including projecting object states from the estimated object states and combining the projected object states with prior sensor scans to determine a conditional probability towards mission accomplishment.

14. A sensor scan planner system as recited in claim 13, further comprising a probability map generated at least in part from previous sensor scans and the projected object states of one or more relocatable targets in a target area, and wherein the sensor scan schedule is generated based at least in part on the probability map.

15. A sensor scan planner system as recited in claim 14, wherein the sensor scan schedule generator is further configured to generate a future time input to develop a projected target location from which the probability map is at least in part generated, the projected target location being based on target characteristics combined with a previously known target location projected into the future by the fixture time input.

16. An article comprising computer readable media encoded with computer executable instructions that, when executed, direct a computing-based system of an airborne platform to:

generate a plurality of sensor scan schedules that the airborne platform could utilize during a mission, each sensor scan schedule correlating scans of multiple sensors for the airborne platform to autonomous target recognition processing;

compare each generated sensor scan schedule to evaluation criteria to determine a contribution towards mission accomplishment that would result from using the that scan schedule along the platform'"'"'s velocity vector each whether the generated schedule is optimal, the evaluation criteria value corresponding to respective previously generated sensor scan schedules; and

use the comparisons to determine a sensor scan schedule that results in a larger contribution toward mission accomplishment.