Large volume holographic imaging systems and associated methods

1. A method for large volume holographic imaging, the method comprising:

decomposing, by processing circuitry, a target volume into a plurality of sub-volumes;

determining, by the processing circuitry, projection operators for points within each sub-volume based on an architecture of a sensor array and a spatial relationship of the sensor array to the target volume;

determining, by the processing circuitry, a point aggregation operator for each sub-volume, the point aggregation operator being an aggregation of the projection operators associated with a respective sub-volume;

receiving, by the processing circuitry, a holographic field measurement data set captured for the target volume via the sensor array;

generating, by the processing circuitry, a sub-volume interest value for each sub-volume by applying the holographic field measurement data set to each point aggregation operator;

determining, by the processing circuitry, a sub-volume with a highest sub-volume interest value;

determining, by the processing circuitry, respective lower-tier sub-volume interest values for lower-tier sub-volumes of the sub-volume with the highest sub-volume interest value, the lower-tier sub-volumes being defined by decomposing the sub-volume with the highest sub-volume interest value; and

generating, by the processing circuitry, an image of the target volume based on the lower-tier sub-volume interest values.

2. The method of claim 1 , further comprising ordering the sub-volumes in a stack, wherein the sub-volume with the highest sub-volume interest value is first in the stack and a sub-volume with a lowest sub-volume interest value is last in the stack.

3. The method of claim 1 , further comprising discarding from further analysis sub-volumes associated with sub-volume interest values that do not satisfy an elimination threshold.

4. The method of claim 1 , wherein generating the image of the target volume based on the lower-tier sub-volume interest values comprises rendering a voxel representation of the lower-tier sub-volumes based on the respective lower-tier sub-volume interest values.

5. The method of claim 1 , wherein determining the point aggregation operators comprises, for each sub-volume, averaging or summing the projection operators for the points within the sub-volume.

6. The method of claim 1 , further comprising determining a lower-tier point aggregation operator for at least one lower-tier sub-volume prior to receiving the holographic field measurement data set.

7. The method of claim 6 , further comprising generating an operator tree structure for the point aggregation operators in association with sub-volumes and the lower-tier point aggregation operators in association with the lower-tier sub-volumes.

8. The method of claim 7 , wherein the target volume and the sub-volumes are decomposed via an octree decomposition.

9. The method of claim 1 , further comprising generating a security alert for security personnel based on the generated image.

10. An apparatus for large volume holographic imaging, the apparatus comprising processing circuitry configured to:

decompose a target volume into a plurality of sub-volumes;

determine projection operators for points within each sub-volume based on an architecture of a sensor array and a spatial relationship of the sensor array to the target volume;

determine a point aggregation operator for each sub-volume, each point aggregation operator being an aggregation of the projection operators associated with a respective sub-volume;

receive holographic field measurement data set captured for the target volume via the sensor array;

generate a sub-volume interest value for each sub-volume by applying the holographic field measurement data set to each point aggregation operator;

determine a sub-volume with a highest sub-volume interest value;

determine respective lower-tier sub-volume interest values for lower-tier sub-volumes of the sub-volume with the highest sub-volume interest value, the lower-tier sub-volumes being defined by decomposing the sub-volume with the highest sub-volume interest value; and

generate an image of the target volume based on the lower-tier sub-volume interest values.

11. The apparatus of claim 10 , wherein the processing circuitry is further configured to order the sub-volumes in a stack, wherein the sub-volume with the highest sub-volume interest value is ordered first in the stack and a sub-volume with a lowest sub-volume interest value is ordered last in the stack.

12. The apparatus of claim 10 , wherein the processing circuitry is further configured to discard from further analysis sub-volumes associated with sub-volume interest values that do not satisfy an elimination threshold.

13. The apparatus of claim 10 , wherein the processing circuitry configured to generate the image of the target volume based on the lower-tier sub-volume interest values includes being configured to render a voxel representation of the lower-tier sub-volumes based on the respective lower-tier sub-volume interest values.

14. The apparatus of claim 10 , wherein the processing circuitry configured to determine the point aggregation operators includes being configured to, for each sub-volume, average or sum the projection operators for the points within the sub-volume.

15. The apparatus of claim 10 , wherein the processing circuitry is further configured to determine a lower-tier point aggregation operator for at least one lower-tier sub-volume prior to receiving the holographic field measurement data set.

16. The apparatus of claim 15 , wherein the processing circuitry is further configured to generate an operator tree structure for the point aggregation operators in association with sub-volumes and the lower-tier point aggregation operators in association with the lower-tier sub-volumes.

17. The apparatus of claim 16 , wherein the processing circuitry is further configured to decompose the target volume and the sub-volumes via an octree decomposition.

18. A holography imaging system comprising:

a holography sensor comprising a sensor array, the holography sensor configured to capture holographic field measurement data set of a target volume; and

processing circuitry configured to:

decompose the target volume into a plurality of sub-volumes;

determine projection operators for points within each sub-volume based on an architecture of the sensor array and a spatial relationship of the sensor array to the target volume;

determine a point aggregation operator for each sub-volume, each point aggregation operator being an aggregation of the projection operators associated with a respective sub-volume;

receive the holographic field measurement data set captured for the target volume via the sensor array;

generate a sub-volume interest value for each sub-volume by applying the holographic field measurement data set to each point aggregation operator;

determine a sub-volume with a highest sub-volume interest value;

determine respective lower-tier sub-volume interest values for lower-tier sub-volumes of the sub-volume with the highest sub-volume interest value, the lower-tier sub-volumes being defined by decomposing the sub-volume with the highest sub-volume interest value; and

generate an image of the target volume based on the lower-tier sub-volume interest values.

19. The holography imaging system of claim 18 , wherein the processing circuitry is further configured to generate an operator tree structure for the point aggregation operators in association with sub-volumes and lower-tier point aggregation operators in association with the lower-tier sub-volumes.

20. The holography imaging system of claim 19 , wherein the processing circuitry is further configured to decompose the target volume and the sub-volumes via an octree decomposition.