SYSTEM AND METHOD FOR MULTI-SENSOR MULTI-TARGET 3D FUSION USING AN UNBIASED MEASUREMENT SPACE

• US 20170350956A1
• Filed: 06/03/2016
• Published: 12/07/2017
• Est. Priority Date: 06/03/2016
• Status: Active Grant

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Karlov, Valeri I., Brody, Julian S., Hulsmann, John D.
• Granted Patent: US 10,371,784 B2
• Time in Patent Office: Days
• CPC Class Codes: G01B 11/002 for measuring two or more coordinates
• CPC Class Codes: G01C 3/08 Use of electric radiation detectors
• CPC Class Codes: G01S 3/785 using adjustment of orientation of directivity characteristics of a detector or detector system to give a desired condition of signal derived from that detector or detector system
• CPC Class Codes: G01S 3/7867 Star trackers navigation using star trackers G01C21/025
• CPC Class Codes: G01S 5/16 using electromagnetic waves other than radio waves

Abstract

System and method for determining a position of a target in an unbiased 3D measurement space: generating 2D measurement data in focal planes of each sensor; calculating a line of sight (LOS) from the target for each sensor; intersecting the LOSs and finding the closest intersection point in a 3D space; calculating a boresight LOS in 3D for each sensor; intersecting the boresight lines of sights for each sensor, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.

Claims

1. A method for determining a position of a target in an unbiased three dimensional (3D) measurement space using sensor data collected against the target, the method comprising:

generating two dimensional (2D) measurement data for the target in focal planes of each of a plurality of sensors;

calculating a line of sight (LOS) to the target for each of the plurality of sensors;

intersecting the calculated lines of sight for each of the plurality of sensor and finding the closest intersection point in a 3D space;

calculating a boresight line of sight in 3D for each of the plurality of sensors;

intersecting the boresight lines of sights for each of the plurality of sensors, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and

forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.

2. The method of claim 1, further comprising utilizing the local unbiased 3D estimates of the position of the target to perform one or more of tracking the target, recognizing the target, and characterizing the target.

3. The method of claim 1, wherein the target is stationary.

4. The method of claim 1, wherein the target is moving and the plurality of sensors are used to measure its position at each point in time.

5. The method of claim 1, wherein at least one of the plurality of sensors includes multiple focal planes for multiple wavebands, wherein the unbiased 3D measurement space is invariant to waveband misalignments in multiple focal planes and to waveband-dependent atmospheric refraction effects.

6. The method of claim 1, wherein the unbiased 3D measurement space is generated from also rates of LOS biases for each of the plurality of sensors, wherein a generalized dynamic triangulation method is used to estimate both positions and velocities of the closest points for the target and the boresight LOS to form the unbiased 3D measurement.

7. The method of claim 1, further comprising a plurality of target candidate pairs;

and associating the plurality of target candidate pairs, wherein associating the plurality of target candidate pairs comprises;

analyzing each of target candidate pairs to determine whether a target candidate pair constitutes a target,

generating differential azimuth/elevation values for each target candidate pair via a projection of the closest points for target candidate pairs and boresight LOSs back into the focal planes of each target candidate pair,

generating a differential range for each target candidate pair via differencing the ranges to the closest points of said each target candidate pair and to the closest point of the boresight LOSs of said each target candidate pair,

transforming the differential azimuth/elevation values and the ranges into the unbiased 3D measurement space, and

searching for two locations in the unbiased 3D measurement space for associating the target candidate pairs, wherein the size of the locations is defined by a sensor resolution in the unbiased 3D measurement space.

8. The method of claim 6, further comprising filtering the local unbiased 3D estimates of the position of the target recursively and in time to continue improving tracking accuracy and target association probabilities as new measurements of the target become available.

9. The method of claim 8, wherein said filtering comprises:

using the local unbiased 3D estimates of the position of the target and target velocity in the unbiased 3D measurement space to model target dynamics in the unbiased 3D measurement space;

using a calibration technique to estimate 2D boresights of each sensor and the 3D boresight of the unbiased 3D measurement space in the absolute coordinate system (ECI);

calculating the position and velocity of the target in the absolute ECI system by adding the local unbiased 3D estimates of the position of the target and the target velocity in the unbiased 3D measurement space to the 3D boresight of the unbiased 3D measurement space.

10. A system for determining a position of a target in an unbiased 3D measurement space using three dimensional (3D) fusion of sensor data collected against the target comprising:

a plurality of sensors, each for generating two dimensional (2D) measurement data for the target in one or more focal planes of said each sensor;

a first processor for calculating a line of sight (LOS) to the target for each of the plurality of sensors; and

a second processor for intersecting the calculated line of sight for each of the plurality of sensor and finding a closest intersection point in a 3D space in a 3D space, calculating a boresight line of sight in 3D for each of the plurality of sensors, intersection the boresight line of sights for each of the plurality of sensors and finding the closest intersection point, in the 3D space to define an origin for forming the unbiased 3D measurement space, and forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target and a closest point of the boresight.

11. The system of claim 10, wherein the first processor and the second processor are the same processor.

12. The system of claim 10, wherein the first processor is located at proximity of at least one of the plurality of sensors, and the second processor is located in a ground platform, an air platform or a sea platform.

13. The system of claim 10, further comprising a third processor for utilizing the local unbiased 3D estimates of the position of the target to perform one or more of tracking the target, recognizing the target, and characterizing the target.

14. The system of claim 10, wherein the target is stationary.

15. The system of claim 10, wherein the target is moving.

16. The system of claim 10, wherein at least one of the plurality of sensors includes multiple focal planes for multiple wavebands.

17. The system of claim 16, wherein the unbiased 3D measurement space is invariant to waveband misalignments in multiple focal planes and to waveband-dependent atmospheric refraction effects.

18. The system of claim 10, wherein the second processor forms the unbiased 3D measurement space from also LOS bias rates of each of the plurality of sensors, wherein a generalized dynamic triangulation method is used to estimate both positions and velocities of the closest points for the target and the boresight LOS to form the unbiased 3D measurement.

19. The system of claim 18, wherein the second processor filters the local unbiased 3D estimates of the position of the target recursively and in time to continue improving tracking accuracy and target association probabilities as new measurements of the target become available.

20. A non-transitory storage medium for storing a set of instructions, the set of instructions when executed by one or more processors perform a method for determining a position of a target in an unbiased three dimensional (3D) measurement space using sensor data collected against a target, the method comprising:

generating two dimensional (2D) measurement data for the target in focal planes of each of a plurality of sensors;

calculating a line of sight (LOS) from the target for each of the plurality of sensors;

intersecting the calculated lines of sight for each of the plurality of sensor and finding the closest intersection point in a 3D space;

calculating a boresight line of sight in 3D for each of the plurality of sensors;

intersecting the boresight lines of sights for each of the plurality of sensors, and finding the closest intersection point in the 3D space to define an origin for forming the unbiased 3D measurement space; and

forming local unbiased 3D estimates of the position of the target in the unbiased 3D measurement space as a difference between a closest point of the target LOS and a closest point of the boresight LOS.