Systems, methods, apparatuses, and devices for identifying, tracking, and managing unmanned aerial vehicles
Systems, methods, and apparatus for performing an action when an aggregated confidence measure. Data is received from a first sensor proximate to a particular air space. Data is also received from a second sensor and a third sensor proximate to the particular air space. The data from the first sensor, second sensor, and third sensor are each analyzed to determine respective confidence measures that a UAV is within the particular air space. The first sensor corresponds to a first type of data, the second sensor corresponds to a second type of data, and the third sensor corresponds to a third type of data. The confidence measures from each sensor are aggregated together to generate a combined confidence measure indicating a possible presence of the UAV within in the particular air space. When the combined confidence measure exceeds a threshold, an action is taken.
1. A system, comprising:
a data store; and
at least one computing device in communication with the data store, the at least one computing device being configured to:
receive first data from a first sensor proximate to a particular air space, the first sensor corresponding to a first type of data;
analyze the first data to determine a first confidence measure that a UAV is within the particular air space;
receive second data from a second sensor proximate to the particular air space, the second sensor corresponding to a second type of data;
analyze the second data to determine a second confidence measure that the UAV is within the particular air space;
receive third data from a third sensor proximate to the particular air space, the third sensor corresponding to a third type of data;
analyze the third data to determine a third confidence measure that the UAV is within the particular air space;
receive fourth data from a fourth sensor proximate to the particular air space, the fourth sensor corresponding to a fourth type of data;
analyze the fourth data to determine a fourth confidence measure that the UAV is within the particular air space;
aggregate the first confidence measure, the second confidence measure, the third confidence measure, and the fourth confidence measure into a combined confidence measure indicating a possible presence of the UAV within in the particular air space; and
upon determination that the combined confidence measure exceeds a predetermined threshold value, perform at least one action.
2. The system of claim 1 , wherein the at least one action comprises one of: ignore the UAV and attempt to locate a controller of the UAV.
3. The system of claim 1 , wherein the at least one computing device is further configured to apply a plurality of rules to at least one of: the first confidence measure, the second confidence measure, and the third confidence measure to determine if the UAV is within the particular air space.
4. The system of claim 3 , wherein the plurality of rules are individually categorized based on a plurality of types of sensors.
5. The system of claim 1 , wherein the at least one computing device is further configured to compare at least one of: the first confidence measure, the second confidence measure, and the third confidence measure to at least one predefined threshold of confidence level.
6. The system of claim 1 , wherein the at least one computing device is further configured to store an indication in the data store that the UAV was identified in the particular air space.
7. The system of claim 1 , wherein the first data type corresponds to video data, the second data type corresponds to audio data, and the third data type corresponds to RF data.
8. A method, comprising:
receiving, via at least one computing device, first data from a first sensor proximate to a particular air space, the first sensor corresponding to a first type of data;
analyzing, via the at least one computing device, the first data to determine a first confidence measure that a UAV is within the particular air space;
receiving, via the at least one computing device, second data from a second sensor proximate to the particular air space, the second sensor corresponding to a second type of data;
analyzing, via the at least one computing device, the second data to determine a second confidence measure that the UAV is within the particular air space;
receiving, via the at least one computing device, third data from a third sensor proximate to the particular air space, the third sensor corresponding to a third type of data;
analyzing, via the at least one computing device, the third data to determine a third confidence measure that the UAV is within the particular air space;
receiving, via the at least one computing device, fourth data from a fourth sensor proximate to the particular air space, the fourth sensor corresponding to a fourth type of data;
analyzing, via the at least one computing device, the fourth data to determine a fourth confidence measure that the UAV is within the particular air space;
generating, via the at least one computing device, a combined confidence measure indicating a possible presence of the UAV within in the particular air space based on the first confidence measure, the second confidence measure, the third confidence measure, and the fourth confidence measure; and
upon determination that the combined confidence measure exceeds a predetermined threshold value, performing, via the at least one computing device, at least one action.
9. The method of claim 8 , wherein analyzing at least one of the first data, the second data, or the third data further comprises:
identifying at least one region of interest (ROI) in at least one video frame, the at least one ROI comprising an image of an object that may be the UAV flying within the particular air space; and
performing an object classification process with respect to the at least one ROI to determine whether the object in the image is the UAV.
10. The method of claim 8 , wherein analyzing at least one of the first data, the second data, or the third data further comprises comparing a pattern identified in at least one RF signal to a respective pattern in each of a plurality of known RF signals.
11. The method of claim 8 , wherein analyzing at least one of the first data, the second data, or the third data further comprises determining if a frequency-to-noise volume for at least one frequency in an audio signal is within a predetermined frequency-to-noise threshold range.
12. The method of claim 8 , wherein the first data type corresponds to image data, the second data type corresponds to RF data, and the third data type corresponds to WiFi data.
13. The method of claim 8 , further comprising performing the at least one action by applying a predetermined rule set.
14. A non-transitory computer-readable medium embodying a program that, when executed by at least one computing device, causes the at least one computing device to:
receive first data from a first sensor proximate to a particular air space, the first sensor corresponding to a first type of data;
analyze the first data to determine a first confidence measure that a UAV is within the particular air space;
receive second data from a second sensor proximate to the particular air space, the second sensor corresponding to a second type of data;
analyze the second data to determine a second confidence measure that the UAV is within the particular air space;
receive third data from a third sensor proximate to the particular air space, the third sensor corresponding to a third type of data;
analyze the third data to determine a third confidence measure that the UAV is within the particular air space;
receive fourth data from a fourth sensor proximate to the particular air space, the fourth sensor corresponding to a fourth type of data;
analyze the fourth data to determine a fourth confidence measure that the UAV is within the particular air space;
generate a combined confidence measure indicating a possible presence of the UAV within in the particular air space based on the first confidence measure, the second confidence measure, the third confidence measure, and the fourth confidence measure; and
upon determination that the combined confidence measure exceeds a predetermined threshold value, store an indication in a database that the UAV was identified in the particular air space.
15. The non-transitory computer-readable medium of claim 14 , wherein the program further causes the at least one computing device to reconfigure at least one of the first sensor or the second sensor subsequent to generating the combined confidence measure.
16. The non-transitory computer-readable medium of claim 14 , wherein the program further causes the at least one computing device to monitor via the first sensor and the second sensor for additional data subsequent to generating the combined confidence measure.
17. The non-transitory computer-readable medium of claim 14 , wherein the program further causes the at least one computing device to:
receive third data from a third sensor proximate to the particular air space, the third sensor corresponding to the second type of data; and
analyze the third data to determine a third confidence measure that the UAV is within the particular air space, wherein generating the combined confidence measure further comprises aggregating the first confidence measure, the second confidence measure, and the third confidence measure into the combined confidence measure.
18. The non-transitory computer-readable medium of claim 14 , wherein the program further causes the at least one computing device to receive background data from the first sensor proximate to the particular air space, wherein analyzing the first data to determine the first confidence measure comprises subtracting the background data from the first data.
19. The system of claim 1 , wherein the at least one computing device is further configured to compare the fourth confidence measure to at least one predefined threshold of confidence level.
20. The system of claim 1 , wherein the at least one computing device is further configured to compare a frequency to noise volume ratio of at least one of: the first data, the second data, the third data, or the fourth data to a predefined threshold.