Threaded Track Method, System, and Computer Program Product

• US 20130229298A1
• Filed: 03/02/2012
• Published: 09/05/2013
• Est. Priority Date: 03/02/2012
• Status: Abandoned Application

• Current Assignee: Mitre Corporation
• Original Assignee: Mitre Corporation
• Inventors: ECKSTEIN, Adric Carlyle, KURCZ, Christopher Edward, SILVA, Marcio Oliveira, WEILAND, William J.
• Application Number:
  US13/411,138
• Publication Number:
  US 20130229298A1
• Time in Patent Office: Days
• US Class Current: 342/107
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/87 Combinations of radar systems, e.g. primary radar and secondary radar

Abstract

A system, method, and computer program product for determining a trajectory of an item includes segmenting surveillance point data of sensors, by sensor, into track segments for the item, associating the track segments for the item in a segment group for the item, and fusing the track segments in the segment group for the item into a synthetic threaded track for the item. The fusing may include filtering of the track segments for the item across track segments. The filtering across track segments may be based on a weighting of the point track data of the track segments for the item. A system for determining a trajectory of an item may include a processing device configured to execute a threaded track process to convert a data set of surveillance point data into a synthetic threaded track for the item.

Claims

1. A method for determining a trajectory of an item, comprising:

segmenting, by sensor, into track segments for the item, surveillance point data of plural sensors for tracking the item, wherein each track segment includes time serial surveillance point data for the item that is associated with a single sensor of the plural sensors;

associating the track segments for the item in a segment group; and

fusing the track segments in the segment group into a synthetic trajectory for the item.

2. The method of claim 1, wherein at least one sensor of the plural sensors is unrelated to at least one other sensor of the plural sensors.

3. The method of claim 1, wherein each sensor of the plural sensors is for tracking the item over at least a portion of the trajectory of the item.

4. The method of claim 1, further comprising parsing the surveillance point data into metadata for the item and point track data for the item.

5. The method of claim 1, wherein the segmenting comprises validating the surveillance point data.

6. The method of claim 5, wherein the validating comprises detecting undesired data in the surveillance point data.

7. The method of claim 6, wherein the detecting undesired data comprises detecting at least one of corrupted data, coasted data, and outlier track point data in the surveillance point data.

8. The method of claim 6, wherein the validating comprises discarding the undesired data in the surveillance point data.

9. The method of claim 5, wherein the validating comprises detecting an outlier track segment.

10. The method of claim 9, wherein the validating comprises discarding the outlier track segment.

11. The method of claim 5, wherein the validating comprises correcting a sensor-based bias of the surveillance point data.

12. The method of claim 11, wherein the sensor-based bias is a predetermined, sensor-specific bias.

13. The method of claim 5, wherein the validating comprises assigning track point weights to the surveillance point data.

14. The method of claim 13, wherein the assigning track point weights to the surveillance point data comprises applying a sensor accuracy model for the sensor that generated the surveillance point data.

15. A method for determining a trajectory of an item, comprising:

receiving track segments for the item, wherein each track segment includes time serial surveillance point data for the item that is associated with a single sensor of plural sensors for tracking the item; and

associating the track segments for the item in a segment group.

16. The method of claim 15, wherein at least one sensor of the plural sensors is unrelated to at least one other sensor of the plural sensors.

17. The method of claim 15, wherein each sensor of the plural sensors is for tracking the item over at least a portion of the trajectory of the item.

18. The method of claim 15, wherein the associating of the track segments includes determining an association between a pair of track segments based on a correlation characteristic, and forming a network of track segments for the item based on the determined association.

19. The method of claim 15, wherein the associating of the track segments comprises determining whether a track segment includes sufficient data for reliably associating the track segment with the segment group.

20. The method of claim 19, wherein the determining comprises at least one of determining whether the track segment includes less than a threshold number of track data points and determining whether the track segment includes metadata sufficient for metadata association.

21. The method of claim 15, wherein the associating of the track segments comprises associating metadata of the track segments for the item.

22. The method of claim 21, wherein the associating of metadata of the track segments comprises matching at least one element of metadata in the surveillance point data for the item.

23. The method of claim 15, wherein the associating of the track segments comprises associating trajectory data of the track segments for the item.

24. The method of claim 23, wherein the associating of trajectory data comprises matching at least one component of metadata in the surveillance point data for the item.

25. The method of claim 23, wherein the associating trajectory data comprises extrapolating segment data for non-overlapping track segments for the item.

26. The method of claim 23, wherein the associating trajectory data comprises interpolating segment data for overlapping track segments for the item.

27. A method for determining a trajectory of an item, comprising:

receiving track segments for the item, the track segments associated in a segment group, wherein each track segment includes time serial surveillance point data for the item that is associated with a single sensor of plural sensors for tracking the item; and

fusing the track segments in the segment group into a synthetic trajectory for the item.

28. The method of claim 27, wherein the fusing comprises filtering across track segments for the item.

29. The method of claim 28, wherein the filtering across track segments comprises at least one of cross track filtering, along track filtering, and vertical track filtering.

30. The method of claim 28, wherein the filtering across track segments comprises windowing track points of the track segments.

31. The method of claim 30, wherein the filtering across track segments comprises weighted least squares filtering of the windowed track points.

32. The method of claim 31, wherein the filtering across track segments comprises applying a trajectory model to the weighted least squares filtering of windowed track points.

33. The method of claim 32, wherein the filtering across track segments comprises applying at least one trajectory model selected from a first order function, a second order function, and a higher order function.

34. The method of claim 32, wherein the filtering across track segments comprises cross-track filtering, and the cross-track filtering comprises applying at least one trajectory model selected from a straight trajectory model, a constant curvature trajectory model, and a variable curvature trajectory model.

35. The method of claim 32, wherein the filtering across track segments comprises along-track filtering, and wherein the along-track filtering comprises applying at least one trajectory model selected from a constant velocity model, a constant acceleration model, and a higher-order variable-acceleration model.

36. The method of claim 32, wherein the filtering across track segments comprises vertical-track filtering, and wherein the vertical-track filtering comprises applying at least one trajectory model selected from a linear climb gradient trajectory model, a linear climb rate trajectory model, and a higher order ascent/descent trajectory model.