Auto-focus acquisition for remote flying targets

1. A system, comprising:

an interface configured to:

receive a radar data;

receive a lens temperature data; and

a processor configured to:

determine a focal setting for a lens system based at least in part on the radar data and the lens temperature data;

provide the focal setting for the lens system;

receive an image taken using the focal setting for the lens system;

determine whether there is an object in the image;

in response to a determination that there is an object in the image:

determine whether the object is in focus; and

in response to a determination that the object is in focus:

indicate that the object is in focus; and

classify and track the object, comprising to:

 train a model using inputted features of the object, wherein the model implements a neural network technique;

 output, using the trained model, parts of the object; and

 determine, based on the outputted parts, whether the object is a threat object or a non-threat object, wherein the non-threat object corresponds with an albatross, a commercial aircraft, or a recreational drone, and wherein the threat object corresponds with a raptor, a military aircraft, or a military drone;

determine whether there is an object in the image that is not in focus;

in response to a determination that an object in the image that is not in focus, indicate that the object is not in focus; and

change the focal setting for the lens system until the object is in focus in response to determining that the image is not in focus, wherein the focal setting for the lens is determined by scanning in one direction and then in another direction.

2. The system of claim 1 , wherein the radar data comprises a radar range distance.

3. The system of claim 2 , wherein determining the focal setting is based at least in part on the radar range distance.

4. The system of claim 1 , wherein determining the focal setting is based at least in part on a look up table, a calculation, or a zoom setting.

5. The system of claim 1 , wherein the radar data comprises an azimuth angle and an elevation angle.

6. The system of claim 5 , wherein a pointing direction is set for the lens system based at least in part on the azimuth angle and the elevation angle.

7. The system of claim 1 , wherein determining that the object in the image is in focus uses a neural network classifier or an edge detection algorithm.

8. A system, comprising:

an interface configured to:

receive a radar data;

receive a lens temperature data; and

a processor configured to:

determine a focal setting for a lens system based at least in part on the radar data and the lens temperature data;

provide the focal setting for the lens system;

receive an image taken using the focal setting for the lens system;

determine whether there is an object in the image;

in response to a determination that there is an object in the image:

determine whether the object is in focus; and

in response to a determination that the object is in focus:

indicate that the object is in focus; and

classify and track the object, comprising to:

 train a model using inputted features of the object, wherein the model implements a neural network technique;

 output, using the trained model, parts of the object; and

 determine, based on the outputted parts, whether the object is a threat object or a non-threat object, wherein the non-threat object corresponds with an albatross, a commercial aircraft, or a recreational drone, and wherein the threat object corresponds with a raptor, a military aircraft, or a military drone; and

in response to a determination that it is unable to determine that there is an object in the image, indicate that it is unknown to determine that there is the object in the image.

9. The system of claim 8 , wherein the processor is further configured to change the focal setting of the lens system in response to a determination that it is unable to determine that there is an object in the image.

10. The system of claim 8 , wherein the processor is further configured to change a zoom setting of the lens system in response to a determination that it is unable to determine that there is an object in the image.

11. The system of claim 10 , wherein the processor is further configured to change a pointing direction of the lens system in response to a determination that it is unable to determine that there is an object in the image.

12. The system of claim 11 , wherein the pointing direction is changed in a search pattern for the object comprising one of the following: a spiral, a square spiral, a raster, an up-down sweep that alternates left of center and right of center, or a Z-shaped pattern.

13. A method, comprising:

receiving a radar data;

receiving a lens temperature data;

determining, using a processor, a focal setting for a lens system based at least in part on the radar data and the lens temperature data;

providing the focal setting for the lens system;

receiving an image taken using the focal setting for the lens system;

determining whether there is an object in the image;

in response to a determination that there is an object in the image:

determining whether the object is in focus; and

in response to a determination that the object is in focus:

indicating that the object is in focus; and

classifying and tracking the object, comprising:

training a model using inputted features of the object, wherein the model implements a neural network technique;

outputting, using the trained model, parts of the object; and

determining, based on the outputted parts, whether the object is a threat object or a non-threat object, wherein the non-threat object corresponds with an albatross, a commercial aircraft, or a recreational drone, and wherein the threat object corresponds with a raptor, a military aircraft, or a military drone;

determining whether there is an object in the image that is not in focus;

in response to a determination that an object in the image that is not in focus, indicating that the object is not in focus; and

changing the focal setting for the lens system until the object is in focus in response to determining that the image is not in focus, wherein the focal setting for the lens is determined by scanning in one direction and then in another direction.

14. A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:

receiving a radar data;

receiving a lens temperature data;

determining, using a processor, a focal setting for a lens system based at least in part on the radar data and the lens temperature data;

providing the focal setting for the lens system;

receiving an image taken using the focal setting for the lens system;

determining whether there is an object in the and image;

in response to a determination that there is an object in the image:

determining whether the object is in image; and

in response to a determination that the object is in focus:

indicating that the object is in focus; and

classifying and tracking the object, comprising:

training a model using inputted features of the object, wherein the model implements a neural network technique;

outputting, using the trained model, parts of the object; and

determining, based on the outputted parts, whether the object is a threat object or a non-threat object, wherein the non-threat object corresponds with an albatross, a commercial aircraft, or a recreational drone, and wherein the threat object corresponds with a raptor, a military aircraft, or a military drone;

determining whether there is an object in the image that is not in focus;

in response to a determination that an object in the image that is not in focus, indicating that the object is not in focus; and

changing the focal setting for the lens system until the object is in focus in response to determining that the image is not in focus, wherein the focal setting for the lens is determined by scanning in one direction and then in another direction.

15. A method, comprising:

receiving a radar data;

receiving a lens temperature data;

determining, using a processor, a focal setting for a lens system based at least in part on the radar data and the lens temperature data;

providing the focal setting for the lens system;

receiving an image taken using the focal setting for the lens system;

determining whether there is an object in the image;

in response to a determination that there is an object in the image:

determining whether the object is in focus; and

in response to a determination that the object is in focus:

indicating that the object is in focus; and

classifying and tracking the object, comprising:

training a model using inputted features of the object, wherein the model implements a neural network technique;

outputting, using the trained model, parts of the object; and

determining, based on the outputted parts, whether the object is a threat object or a non-threat object, wherein the non-threat object corresponds with an albatross, a commercial aircraft, or a recreational drone, and wherein the threat object corresponds with a raptor, a military aircraft, or a military drone; and

in response to a determination that it is unable to determine that there is an object in the image, indicate that it is unknown to determine that there is the object in the image.

16. A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:

receiving a radar data;

receiving a lens temperature data;

determining, using a processor, a focal setting for a lens system based at least in part on the radar data and the lens temperature data;

providing the focal setting for the lens system;

receiving an image taken using the focal setting for the lens system;

determining whether there is an object in the image;

in response to a determination that there is an object in the image:

determining whether the object is in focus; and

in response to a determination that the object is in focus:

indicating that the object is in focus; and

classifying and tracking the object, comprising:

training a model using inputted features of the object, wherein the model implements a neural network technique;

outputting, using the trained model, parts of the object; and

determining, based on the outputted parts, whether the object is a threat object or a non-threat object, wherein the non-threat object corresponds with an albatross, a commercial aircraft, or a recreational drone, and wherein the threat object corresponds with a raptor, a military aircraft, or a military drone; and

in response to a determination that it is unable to determine that there is an object in the image, indicate that it is unknown to determine that there is the object in the image.