Trajectory prediction

• US 20060224318A1
• Filed: 03/30/2005
• Published: 10/05/2006
• Est. Priority Date: 03/30/2005
• Status: Abandoned Application

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Whitley, Ted D., Estkowski, Regina, Wilson, Robert C. Jr.
• Application Number:
  US11/096,251
• Publication Number:
  US 20060224318A1
• Time in Patent Office: Days
• US Class Current: 701/213
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/86 Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• CPC Class Codes: G01S 5/0027 of actual mobile position, i.e. position determined on mobile
• CPC Class Codes: G08G 5/0052 for cruising combined instruments indicating more than one navigational value G01C23/00

Abstract

Methods and systems for improved prediction of the movement of vehicles through an airspace are disclosed. In one embodiment, a method for predicting movement of a vehicle in an airspace from a first location to a second location, the predicting based upon at least one data signal configured to communicate a plurality of sensor-perceived first locations of the vehicle includes time correlating the plurality of sensor perceived first locations. The plurality of sensor-perceived first locations are particle filtered to generate a plurality of confidences each corresponding with one of a plurality of predicted locations, each confidence representing a likelihood that the corresponding predicted location includes the second location.

Claims

1. A method for predicting movement of a vehicle in an airspace from a first location to a second location, comprising:

receiving at least one data signal indicative of a plurality of sensor-perceived first locations of the vehicle;

time correlating the plurality of sensor-perceived first locations of the vehicle; and

particle-filtering the plurality of sensor-perceived first locations to generate a plurality of confidences each corresponding with one of a plurality of predicted locations, each confidence representing a likelihood that the corresponding predicted location includes the second location.

2. The method of claim 1, wherein receiving at least one data signal includes receiving at least one modulated data signal indicative of a plurality of sensor-perceived first locations of the vehicle.

3. The method of claim 1, wherein at least one of the plurality of sensor-perceived first locations is derived by means of at least one of processing a radar return and processing a GPS sensor on-board the vehicle.

4. The method of claim 1, wherein at least one of the plurality of sensor-perceived first locations is derived by means of a surveillance satellite observation.

5. The method of claim 1, wherein at least one of the plurality of signals includes at least one retrieved model of the vehicle.

6. The method of claim 5, wherein the receiving at least one is receiving data signals according to the at least one retrieved model of the vehicle.

7. The method of claim 6, wherein the receiving at least one is receiving data signals according to the retrieved model of the vehicle includes excluding data signals from particle filtering based upon the at least one retrieved model of the vehicle.

8. The method of claim 5, wherein the particle filtering is based upon the at least one model of the aircraft.

9. A processor configured to predict movement of a vehicle in an airspace from a first location to a second location, comprising:

a receiving component configured to receive at least one data signal indicative of a plurality of sensor-perceived first locations of the vehicle;

a time correlating component configured to correlate the plurality of sensor-perceived first locations of the vehicle according to a time of perception; and

a particle-filtering component configured to filter the plurality of sensor-perceived first locations by particle filtering means to generate a plurality of confidences each corresponding with one of a plurality of predicted locations, each confidence representing a likelihood that the corresponding predicted location includes the second location.

10. The processor of claim 9, wherein the receiving component is configured to receive at least one modulated data signal indicative of a plurality of sensor-perceived first locations of the vehicle.

11. The processor of claim 9, wherein at least one of the plurality of sensor-perceived first locations is derived by means of at least one of processing a radar return and processing a GPS sensor on-board the vehicle.

12. The processor of claim 9, wherein at least one of the plurality of sensor-perceived first locations is derived by means of a surveillance satellite observation.

13. The processor of claim 9, wherein at least one of the plurality of signals includes at least one retrieved model of the vehicle.

14. The processor of claim 13, wherein the particle filter component includes a filter to suppress signals according to the at least one retrieved model of the vehicle.

15. The processor of claim 13, wherein the particle filter component selects signals for filtering according to the at least one received model of the vehicle.

16. The processor of claim 13, wherein the one of a plurality of signals are assigned a weighting value according to the at least one retrieved model of the vehicle.

17. A method of constructing a path for movement of a vehicle in the presence of at least one hazard in an airspace from a first location to a second location, comprising:

monitoring an airspace with sensors configured to perceive a location of the vehicle in the airspace;

generating a data signal according the location perceived by the sensors;

filtering the data signal according to a particle filter algorithm to yield a location confidence function; and

truncating the possible locations according to the location confidence function and a threshold value to yield a location cloud.

18. The method of claim 17, wherein generating a data signal includes generating a modulated data signal.

19. The method of claim 17, wherein truncating includes truncating the possible locations for a plurality of the vehicle to produce a plurality of location clouds.

20. The method of claim 17, wherein filtering the data signal includes retrieving a model corresponding to the vehicle.

21. The method of claim 20, wherein filtering the data signal is filtering according to the retrieved model.

22. The method of claim 17, wherein the filtering is particle filtering.