Systems, methods, apparatuses, and devices for identifying and tracking unmanned aerial vehicles via a plurality of sensors

• US 9,805,273 B1
• Filed: 11/08/2016
• Issued: 10/31/2017
• Est. Priority Date: 11/08/2016
• Status: Active Grant

• Current Assignee: Dedrone Holdings, Inc.
• Original Assignee: Dedrone Holdings, Inc.
• Inventors: Seeber, Rene, Seebach, Ingo, Meyer, Henning, Schoeler, Markus, Baumgart, Kai, Scheibe, Christian, Prantl, David
• Primary Examiner(s): Williams, Jeffery
• Application Number:
  US15/346,251
• Time in Patent Office: 357 Days
• Field of Search: 348144
• CPC Class Codes: G01S 11/06 using intensity measurements
• CPC Class Codes: G01S 5/0249 Determining position using measurements made by a non-stationary device other than the device whose position is being determined
• CPC Class Codes: G01S 5/0295 Proximity-based methods, e.g. position inferred from reception of particular signals
• CPC Class Codes: G06F 2218/16 by matching signal segments
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/223 using block-matching
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G08G 5/0026 located on the ground
• CPC Class Codes: G08G 5/0069 specially adapted for an unmanned aircraft
• CPC Class Codes: G08G 5/0082 for monitoring traffic from a ground station radar or analogous systems specially adapted for traffic control G01S13/91
• CPC Class Codes: H04R 29/00 Monitoring arrangements; Testing arrangements for hearing aids H04R25/30; detection of loudspeaker connection H04R5/04; sound-field adaptation dependent on speaker detection H04S7/308

Abstract

Systems, methods, and apparatus for identifying and tracking UAVs including a plurality of sensors operatively connected over a network to a configuration of software and/or hardware. Generally, the plurality of sensors monitors a particular environment and transmits the sensor data to the configuration of software and/or hardware. The data from each individual sensor can be directed towards a process configured to best determine if a UAV is present or approaching the monitored environment. The system generally allows for a detected UAV to be tracked, which may allow for the system or a user of the system to predict how the UAV will continue to behave over time. The sensor information as well as the results generated from the systems and methods may be stored in one or more databases in order to improve the continued identifying and tracking of UAVs.

Claims

1. A method for identifying unmanned aerial vehicles (UAVs) in a particular air space, comprising the steps of:

receiving video data from a particular video sensor proximate to the particular air space, the video data including at least one image of an object that may be a UAV flying within the particular air space;

analyzing the video data to determine a first confidence measure that the object in the at least one image comprises a UAV;

receiving audio signal data from a particular audio sensor proximate to the particular air space, the audio signal data including frequency data indicating a possible presence of a UAV within the particular air space;

analyzing the audio signal data to determine a second confidence measure that the frequency data comprises a UAV;

receiving radio frequency (RF) signal data from a particular RF sensor proximate to the particular air space, the RF signal data including data indicating a possible presence of a UAV within the particular air space;

analyzing the RF signal data to determine a third confidence measure that the RF signal data corresponds to a UAV;

aggregating the first confidence measure, the second confidence measure, and the third confidence measure into a combined confidence measure indicating a possible presence of a UAV in the particular air space; and

upon determination that the combined confidence measure exceeds a predetermined threshold value, storing an indication in a database that a UAV was identified in the particular air space.

2. The method of claim 1, wherein the step of analyzing the RF signal data to determine the third confidence measure further comprises the steps of:

filtering the RF signal data to remove one or more unwanted frequencies;

decoding the filtered RF signal data to generate a pattern of one or more frequencies and one or more amplitudes representing the RF signal data;

comparing the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data to known patterns of frequencies and amplitudes known to be associated with UAVs; and

upon determination that the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data substantially matches at least one of the known patterns, determining the third confidence measure.

3. The method of claim 1, further comprising the steps of:

receiving Wi-Fi signal data from a particular Wi-Fi sensor proximate to the particular air space, the Wi-Fi signal data including data indicating a possible presence of a UAV within the particular air space;

analyzing the Wi-Fi signal data to determine a fourth confidence measure that the Wi-Fi signal data corresponds to a UAV; and

aggregating the fourth confidence measure into the combined confidence measure.

4. The method of claim 3, wherein the step of analyzing the Wi-Fi signal data to determine the fourth confidence measure further comprises the steps of:

extracting a media access control (MAC) address from the Wi-Fi signal data;

comparing the extracted MAC address to one or more known MAC addresses known to be associated with UAVs; and

upon determination that the extracted MAC address substantially matches at least one known MAC address, determining the fourth confidence measure.

5. The method of claim 3, wherein the step of analyzing the Wi-Fi signal data to determine the fourth confidence measure further comprises the steps of:

extracting a service set identifier (SSID) from the Wi-Fi signal data;

comparing the extracted SSID to one or more known SSIDs known to be associated with UAVs; and

upon determination that the extracted SSID substantially matches at least one known SSID, determining the fourth confidence measure.

6. The method of claim 3, wherein the step of analyzing the Wi-Fi signal data to determine the fourth confidence measure further comprises the steps of:

extracting a received signal strength indicator (RSSI) from the Wi-Fi signal data; and

based on the extracted RSSI, estimating a physical distance of the object emanating the Wi-Fi signal data from the particular Wi-Fi sensor,

whereby the physical distance must be above a predetermined threshold distance value to indicate the presence of a UAV.

7. The method of claim 1, wherein the step of analyzing the video data to determine a first confidence measure further comprises the steps of:

identifying at least one region of interest (ROI) in at least one video frame in the video data, the at least one ROI comprising the image of the object that may be a UAV flying within the particular air space;

performing an object classification process with respect to the at least one ROI to determine whether the object in the image is a UAV, the object classification process comprising the steps of;

extracting image data from the image of the at least one ROI;

comparing the extracted image data to prior image data of objects known to be UAVs to determine a probability that the object in the image is a UAV; and

upon determination that the probability that the object in the image is a UAV exceeds a predetermined threshold, determining the first confidence measure.

8. The method of claim 1, wherein the step of analyzing the audio data to determine a second confidence measure further comprises the steps of:

converting the audio signal data to frequency domain data such that the audio signal data may be represented as one or more frequencies;

determining if a frequency-to-noise volume for each of the one or more frequencies is within a predetermined frequency-to-noise threshold range;

upon determination that a respective frequency-to-noise volume for a respective frequency of the converted audio signal data is within the predetermined frequency-to-noise threshold range, comparing the respective frequency to one or more UAV frequencies known to be associated with UAVs; and

upon determination that the respective frequency substantially matches at least one of the one or more UAV frequencies known to be associated with UAVs, determining the second confidence measure.

9. The method of claim 1, further comprising the step of storing in the database the video data and audio signal data in association with the indication that the UAV was identified in the particular air space.

10. The method of claim 1, further comprising the step of initiating an alert to a system user that a UAV has been detected in the particular air space.

11. The method of claim 1, wherein the predetermined threshold value comprises a percentage.

12. The method of claim 1, wherein the particular video sensor and the particular audio sensor are enclosed in a unitary housing.

13. A system for identifying unmanned aerial vehicles (UAVs) in a particular air space, comprising:

a video sensor proximate to the particular air space, wherein the video sensor is configured to collect and transmit video data, the video data including at least one image of an object that may be a UAV flying within the particular air space;

an audio sensor proximate to the particular air space, wherein the audio sensor is configured to collect and transmit audio signal data, the audio signal data including at least frequency data indicating a possible presence of a UAV within the particular air space;

a radio frequency (RF) sensor proximate to the particular air space, wherein the RF sensor is configured to collect RF signal data, the RF signal data including at least data indicating a possible presence of a UAV within the particular air space;

a database; and

a processor operatively coupled to the video sensor, the audio sensor, the RF sensor, and the database, wherein the processor is operative to;

analyze the video data to determine a first confidence measure that the object in the at least one image comprises a UAV;

analyze the audio signal data to determine a second confidence measure that the frequency data comprises a UAV;

analyze the RF signal data to determine a third confidence measure that the RF signal data corresponds to a UAV;

aggregate the first confidence measure, the second confidence measure, and the third confidence measure into a combined confidence measure indicating a possible presence of a UAV in the particular air space; and

upon determination that the combined confidence measure exceeds a predetermined threshold value, store an indication in the database that a UAV was identified in the particular air space.

14. The system of claim 13, wherein the processor is further operative to:

filter the RF signal data to remove one or more unwanted frequencies;

decode the filtered RF signal to generate a pattern of one or more frequencies and one or more amplitudes representing the RF signal data;

compare the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data to known patterns of frequencies and amplitudes known to be associated with UAVs; and

upon determination that the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data substantially matches at least one of the known patterns, determine the third confidence measure.

15. The system of claim 13, the system further comprising:

a Wi-Fi sensor proximate to the particular air space, wherein the Wi-Fi sensor is configured to receive Wi-Fi signal data, the Wi-Fi signal data including data indicating a possible presence of a UAV within the particular air space; and

the processor further operatively coupled to the Wi-Fi sensor, the processor further operative to;

analyze the Wi-Fi signal data to determine a fourth confidence measure that the Wi-Fi signal data corresponds to a UAV; and

aggregate the fourth confidence measure into the combined confidence measure.

16. The system of claim 15, wherein the processor is further operative to:

extract a media access control (MAC) address from the Wi-Fi signal data;

compare the extracted MAC address to one or more known MAC addresses known to be associated with UAVs; and

;

upon determination that the extracted MAC address substantially matches at least one known MAC address, determine the fourth confidence measure.

17. The system of claim 15, wherein the processor is further operative to:

extract a service set identifier (SSID) from the Wi-Fi signal data;

compare the extracted SSID to one or more known SSIDs known to be associated with UAVs; and

upon determination that the extracted SSID substantially matches at least one known SSID, determining the fourth confidence measure.

18. The system of claim 15, wherein the processor is further operative to:

extract a received signal strength indicator (RSSI) from the Wi-Fi signal data; and

based on the extracted RSSI, estimate a physical distance of the object emanating the Wi-Fi signal data from the particular Wi-Fi sensor;

whereby the physical distance must be above a predetermined threshold distance value to indicate the presence of a UAV.

19. The system of claim 13, wherein the processor is further operative to:

identify at least one region of interest (ROI) in at least one video frame in the video data, the at least one ROI comprising the image of the object that may be a UAV flying within the particular air space; and

perform an object classification process with respect to the at least one ROI to determine whether the object in the image is a UAV, the object classification process comprising the steps of;

extracting image data from the image of the at least one ROI;

comparing the extracted image data to prior image data of objects known to be UAVs to determine a probability that the object in the image is a UAV; and

upon determination that the probability that the object in the image is a UAV exceeds a predetermined threshold, determining the first confidence measure.

20. The system of claim 13, wherein in addition to being operative to analyze the audio data to determine a second confidence measure, the processor is further operative to:

convert the audio signal data to frequency domain data such that the audio signal data may be represented as one or more frequencies;

determine if a frequency-to-noise volume for each of the one or more frequencies is within a predetermined frequency-to-noise volume threshold range;

upon determination that a respective frequency-to-noise volume for a respective frequency of the converted audio signal data is within the predetermined frequency-to-noise threshold range, compare the respective frequency to one or more UAV frequencies known to be associated with UAVs; and

upon determination that the respective frequency substantially matches at least one of the one or more UAV frequencies known to be associated with UAVs, determine the second confidence measure.

21. The system of claim 13, wherein the processor is further operative to store in the database the video data and audio signal data in association with the indication that the UAV was identified in the particular air space.

22. The system of claim 13, wherein the processor is further operative to alert a system user that a UAV has been detected in the particular air space.

23. The system of claim 13, wherein the predetermined threshold value comprises a percentage.

24. The system of claim 13, wherein the video and audio sensor are enclosed in a unitary housing.

25. A method for identifying unmanned aerial vehicles (UAVs) in a particular air space, comprising the steps of:

receiving video data from a particular video sensor proximate to the particular air space, the video data including at least one image of an object that may be a UAV flying within the particular air space;

analyzing the video data to determine a first confidence measure that the object in the at least one image comprises a UAV;

receiving audio signal data from a particular audio sensor proximate to the particular air space, the audio signal data including frequency data indicating a possible presence of a UAV within the particular air space;

analyzing the audio signal data to determine a second confidence measure that the frequency data comprises a UAV;

receiving Wi-Fi signal data from a particular Wi-Fi sensor proximate to the particular air space, the Wi-Fi signal data including data indicating a possible presence of a UAV within the particular air space;

analyzing the Wi-Fi signal data to determine a third confidence measure that the Wi-Fi signal data corresponds to a UAV;

aggregating the first confidence measure, the second confidence measure, and the third confidence measure into a combined confidence measure indicating a possible presence of a UAV in the particular air space;

upon determination that the combined confidence measure exceeds a predetermined threshold value, storing an indication in a database that a UAV was identified in the particular air space.

26. The method of claim 25, further comprising the steps of:

receiving radio frequency (RF) signal data from a particular RF sensor proximate to the particular air space, the RF signal data including data indicating a possible presence of a UAV within the particular air space;

analyzing the RF signal data to determine a fourth confidence measure that the RF signal data corresponds to a UAV; and

aggregating the fourth confidence measure into the combined confidence measure.

27. The method of claim 26, wherein the step of analyzing the RF signal data to determine the fourth confidence measure further comprises the steps of:

filtering the RF signal data to remove one or more unwanted frequencies;

decoding the filtered RF signal data to generate a pattern of one or more frequencies and one or more amplitudes representing the RF signal data;

comparing the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data to known patterns of frequencies and amplitudes known to be associated with UAVs; and

upon determination that the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data substantially matches at least one of the known patterns, determining the fourth confidence measure.

28. The method of claim 25, wherein the step of analyzing the Wi-Fi signal data to determine the third confidence measure further comprises the steps of:

extracting a media access control (MAC) address from the Wi-Fi signal data;

comparing the extracted MAC address to one or more known MAC addresses known to be associated with UAVs; and

upon determination that the extracted MAC address substantially matches at least one known MAC address, determining the third confidence measure.

29. The method of claim 25, wherein the step of analyzing the Wi-Fi signal data to determine the third confidence measure further comprises the steps of:

extracting a service set identifier (SSID) from the Wi-Fi signal data;

comparing the extracted SSID to one or more known SSIDs known to be associated with UAVs; and

upon determination that the extracted SSID substantially matches at least one known SSID, determining the third confidence measure.

30. The method of claim 25, wherein the step of analyzing the Wi-Fi signal data to determine the third confidence measure further comprises the steps of:

extracting a received signal strength indicator (RSSI) from the Wi-Fi signal data; and

based on the extracted RSSI, estimating a physical distance of the object emanating the Wi-Fi signal data from the particular Wi-Fi sensor,

whereby the physical distance must be above a predetermined threshold distance value to indicate the presence of a UAV.

31. The method of claim 25, wherein the step of analyzing the video data to determine a first confidence measure further comprises the steps of:

identifying at least one region of interest (ROI) in at least one video frame in the video data, the at least one ROI comprising the image of the object that may be a UAV flying within the particular air space;

performing an object classification process with respect to the at least one ROI to determine whether the object in the image is a UAV, the object classification process comprising the steps of;

extracting image data from the image of the at least one ROI;

comparing the extracted image data to prior image data of objects known to be UAVs to determine a probability that the object in the image is a UAV; and

upon determination that the probability that the object in the image is a UAV exceeds a predetermined threshold, determining the first confidence measure.

32. The method of claim 25, wherein the step of analyzing the audio data to determine a second confidence measure further comprises the steps of:

converting the audio signal data to frequency domain data such that the audio signal data may be represented as one or more frequencies;

determining if a frequency-to-noise volume for each of the one or more frequencies is within a predetermined frequency-to-noise threshold range;

upon determination that a respective frequency-to-noise volume for a respective frequency of the converted audio signal data is within the predetermined frequency-to-noise threshold range, comparing the respective frequency to one or more UAV frequencies known to be associated with UAVs; and

upon determination that the respective frequency substantially matches at least one of the one or more UAV frequencies known to be associated with UAVs, determining the second confidence measure.

33. The method of claim 25, further comprising the step of storing in the database the video data and audio signal data in association with the indication that the UAV was identified in the particular air space.

34. The method of claim 25, further comprising the step of initiating an alert to a system user that a UAV has been detected in the particular air space.

35. The method of claim 25, wherein the predetermined threshold value comprises a percentage.

36. The method of claim 25, wherein the particular video sensor and the particular audio sensor are enclosed in a unitary housing.

37. A method for identifying unmanned aerial vehicles (UAVs) in a particular air space, comprising the steps of:

receiving video data from a particular video sensor proximate to the particular air space, the video data including at least one image of an object that may be a UAV flying within the particular air space;

analyzing the video data to determine a first confidence measure that the object in the at least one image comprises a UAV;

receiving audio signal data from a particular audio sensor proximate to the particular air space, the audio signal data including frequency data indicating a possible presence of a UAV within the particular air space;

analyzing the audio signal data to determine a second confidence measure that the frequency data comprises a UAV, wherein analyzing the audio signal data comprises the steps of;

converting the audio signal data to frequency domain data such that the audio signal data may be represented as one or more frequencies;

determining if a frequency-to-noise volume for each of the one or more frequencies is within a predetermined frequency-to-noise threshold range;

upon determination that a respective frequency-to-noise volume for a respective frequency of the converted audio signal data is within the predetermined frequency-to-noise threshold range, comparing the respective frequency to one or more UAV frequencies known to be associated with UAVs; and

upon determination that the respective frequency substantially matches at least one of the one or more UAV frequencies known to be associated with UAVs, determining the second confidence measure;

aggregating the first confidence measure and the second confidence measure into a combined confidence measure indicating a possible presence of a UAV in the particular air space; and

upon determination that the combined confidence measure exceeds a predetermined threshold value, storing an indication in a database that a UAV was identified in the particular air space.

38. The method of claim 37, further comprising the steps of:

receiving radio frequency (RF) signal data from a particular RF sensor proximate to the particular air space, the RF signal data including data indicating a possible presence of a UAV within the particular air space;

analyzing the RF signal data to determine a third confidence measure that the RF signal data corresponds to a UAV; and

aggregating the third confidence measure into the combined confidence measure.

39. The method of claim 38, wherein the step of analyzing the RF signal data to determine the third confidence measure further comprises the steps of:

filtering the RF signal data to remove one or more unwanted frequencies;

decoding the filtered RF signal data to generate a pattern of one or more frequencies and one or more amplitudes representing the RF signal data;

comparing the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data to known patterns of frequencies and amplitudes known to be associated with UAVs; and

upon determination that the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data substantially matches at least one of the known patterns, determining the third confidence measure.

40. The method of claim 37, further comprising the steps of:

receiving Wi-Fi signal data from a particular Wi-Fi sensor proximate to the particular air space, the Wi-Fi signal data including data indicating a possible presence of a UAV within the particular air space;

analyzing the Wi-Fi signal data to determine a fourth confidence measure that the Wi-Fi signal data corresponds to a UAV; and

aggregating the fourth confidence measure into the combined confidence measure.

41. The method of claim 40, wherein the step of analyzing the Wi-Fi signal data to determine the fourth confidence measure further comprises the steps of:

extracting a media access control (MAC) address from the Wi-Fi signal data;

comparing the extracted MAC address to one or more known MAC addresses known to be associated with UAVs; and

upon determination that the extracted MAC address substantially matches at least one known MAC address, determining the fourth confidence measure.

42. The method of claim 40, wherein the step of analyzing the Wi-Fi signal data to determine the fourth confidence measure further comprises the steps of:

extracting a service set identifier (SSID) from the Wi-Fi signal data;

comparing the extracted SSID to one or more known SSIDs known to be associated with UAVs; and

upon determination that the extracted SSID substantially matches at least one known SSID, determining the fourth confidence measure.

43. The method of claim 40, wherein the step of analyzing the Wi-Fi signal data to determine the fourth confidence measure further comprises the steps of:

extracting a received signal strength indicator (RSSI) from the Wi-Fi signal data; and

based on the extracted RSSI, estimating a physical distance of the object emanating the Wi-Fi signal data from the particular Wi-Fi sensor,

whereby the physical distance must be above a predetermined threshold distance value to indicate the presence of a UAV.

44. The method of claim 37, wherein the step of analyzing the video data to determine a first confidence measure further comprises the steps of:

identifying at least one region of interest (ROI) in at least one video frame in the video data, the at least one ROI comprising the image of the object that may be a UAV flying within the particular air space;

performing an object classification process with respect to the at least one ROI to determine whether the object in the image is a UAV, the object classification process comprising the steps of;

extracting image data from the image of the at least one ROI;

comparing the extracted image data to prior image data of objects known to be UAVs to determine a probability that the object in the image is a UAV; and

upon determination that the probability that the object in the image is a UAV exceeds a predetermined threshold, determining the first confidence measure.

45. The method of claim 37, further comprising the step of storing in the database the video data and audio signal data in association with the indication that the UAV was identified in the particular air space.

46. The method of claim 37, further comprising the step of initiating an alert to a system user that a UAV has been detected in the particular air space.

47. The method of claim 37, wherein the predetermined threshold value comprises a percentage.

48. The method of claim 37, wherein the particular video sensor and the particular audio sensor are enclosed in a unitary housing.

49. A system for identifying unmanned aerial vehicles (UAVs) in a particular air space, comprising:

a video sensor proximate to the particular air space, wherein the video sensor is configured to collect and transmit video data, the video data including at least one image of an object that may be a UAV flying within the particular air space;

an audio sensor proximate to the particular air space, wherein the audio sensor is configured to collect and transmit audio signal data, the audio signal data including at least frequency data indicating a possible presence of a UAV within the particular air space;

a Wi-Fi sensor proximate to the particular air space, wherein the Wi-Fi sensor is configured to receive Wi-Fi signal data, the Wi-Fi signal data including data indicating a possible presence of a UAV within the particular air space;

a database; and

a processor operatively coupled to the video sensor, the audio sensor, the Wi-Fi sensor, and the database, wherein the processor is operative to;

analyze the video data to determine a first confidence measure that the object in the at least one image comprises a UAV;

analyze the audio signal data to determine a second confidence measure that the frequency data comprises a UAV;

analyze the Wi-Fi signal data to determine a third confidence measure that the Wi-Fi signal data corresponds to a UAV;

aggregate the first confidence measure, the second confidence measure, and the third confidence measure into a combined confidence measure indicating a possible presence of a UAV in the particular air space; and

upon determination that the combined confidence measure exceeds a predetermined threshold value, store an indication in the database that a UAV was identified in the particular air space.

50. The system of claim 49, further comprising:

a radio frequency (RF) sensor proximate to the particular air space, wherein the RF sensor is configured to collect RF signal data, the RF signal data including at least data indicating a possible presence of a UAV within the particular air space;

the processor further operatively coupled to the RF sensor, wherein the processor is further operative to;

analyze the RF signal data to determine a fourth confidence measure that the RF signal data corresponds to a UAV; and

aggregate the fourth confidence measure into the combined confidence measure.

51. The system of claim 50, wherein the processor is further operative to:

filter the RF signal data to remove one or more unwanted frequencies;

decode the filtered RF signal to generate a pattern of one or more frequencies and one or more amplitudes representing the RF signal data;

compare the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data to known patterns of frequencies and amplitudes known to be associated with UAVs; and

upon determination that the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data substantially matches at least one of the known patterns, determine the fourth confidence measure.

52. The system of claim 49, wherein the processor is further operative to:

extract a media access control (MAC) address from the Wi-Fi signal data;

compare the extracted MAC address to one or more known MAC addresses known to be associated with UAVs; and

;

upon determination that the extracted MAC address substantially matches at least one known MAC address, determine the third confidence measure.

53. The system of claim 49, wherein the processor is further operative to:

extract a service set identifier (SSID) from the Wi-Fi signal data;

compare the extracted SSID to one or more known SSIDs known to be associated with UAVs; and

upon determination that the extracted SSID substantially matches at least one known SSID, determining the third confidence measure.

54. The system of claim 49, wherein the processor is further operative to:

extract a received signal strength indicator (RSSI) from the Wi-Fi signal data; and

based on the extracted RSSI, estimate a physical distance of the object emanating the Wi-Fi signal data from the particular Wi-Fi sensor;

whereby the physical distance must be above a predetermined threshold distance value to indicate the presence of a UAV.

55. The system of claim 49, wherein the processor is further operative to:

identify at least one region of interest (ROI) in at least one video frame in the video data, the at least one ROI comprising the image of the object that may be a UAV flying within the particular air space; and

perform an object classification process with respect to the at least one ROI to determine whether the object in the image is a UAV, the object classification process comprising the steps of;

extracting image data from the image of the at least one ROI;

comparing the extracted image data to prior image data of objects known to be UAVs to determine a probability that the object in the image is a UAV; and

upon determination that the probability that the object in the image is a UAV exceeds a predetermined threshold, determining the first confidence measure.

56. The system of claim 49, wherein the processor is further operative to:

convert the audio signal data to frequency domain data such that the audio signal data may be represented as one or more frequencies;

determine if a frequency-to-noise volume for each of the one or more frequencies is within a predetermined frequency-to-noise volume threshold range;

upon determination that a respective frequency-to-noise volume for a respective frequency of the converted audio signal data is within the predetermined frequency-to-noise threshold range, compare the respective frequency to one or more UAV frequencies known to be associated with UAVs; and

upon determination that the respective frequency substantially matches at least one of the one or more UAV frequencies known to be associated with UAVs, determine the second confidence measure.

57. The system of claim 49, wherein the processor is further operative to store in the database the video data and audio signal data in association with the indication that the UAV was identified in the particular air space.

58. The system of claim 49, wherein the processor is further operative to alert a system user that a UAV has been detected in the particular air space.

59. The system of claim 49, wherein the predetermined threshold value comprises a percentage.

60. The system of claim 49, wherein the video and audio sensor are enclosed in a unitary housing.

61. A system for identifying unmanned aerial vehicles (UAVs) in a particular air space, comprising:

a video sensor proximate to the particular air space, wherein the video sensor is configured to collect and transmit video data, the video data including at least one image of an object that may be a UAV flying within the particular air space;

an audio sensor proximate to the particular air space, wherein the audio sensor is configured to collect and transmit audio signal data, the audio signal data including at least frequency data indicating a possible presence of a UAV within the particular air space;

a database; and

a processor operatively coupled to the video sensor, the audio sensor, and the database, wherein the processor is operative to;

analyze the video data to determine a first confidence measure that the object in the at least one image comprises a UAV;

analyze the audio signal data to determine a second confidence measure that the frequency data comprises a UAV, wherein analyzing the audio signal data further comprises;

converting the audio signal data to frequency domain data such that the audio signal data may be represented as one or more frequencies;

determining if a frequency-to-noise volume for each of the one or more frequencies is within a predetermined frequency-to-noise volume threshold range;

upon determination that a respective frequency-to-noise volume for a respective frequency of the converted audio signal data is within the predetermined frequency-to-noise threshold range, comparing the respective frequency to one or more UAV frequencies known to be associated with UAVs; and

upon determination that the respective frequency substantially matches at least one of the one or more UAV frequencies known to be associated with UAVs, determine the second confidence measure;

aggregate the first confidence measure and the second confidence measure into a combined confidence measure indicating a possible presence of a UAV in the particular air space; and

upon determination that the combined confidence measure exceeds a predetermined threshold value, store an indication in the database that a UAV was identified in the particular air space.

62. The system of claim 61, further comprising:

a radio frequency (RF) sensor proximate to the particular air space, wherein the RF sensor is configured to collect RF signal data, the RF signal data including at least data indicating a possible presence of a UAV within the particular air space;

the processor further operatively coupled to the RF sensor, wherein the processor is further operative to;

analyze the RF signal data to determine a third confidence measure that the RF signal data corresponds to a UAV; and

aggregate the third confidence measure into the combined confidence measure.

63. The system of claim 62, wherein the processor is further operative to:

filter the RF signal data to remove one or more unwanted frequencies;

decode the filtered RF signal to generate a pattern of one or more frequencies and one or more amplitudes representing the RF signal data;

compare the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data to known patterns of frequencies and amplitudes known to be associated with UAVs; and

upon determination that the pattern of the one or more frequencies and the one or more amplitudes representing the RF signal data substantially matches at least one of the known patterns, determine the third confidence measure.

64. The system of claim 61, the system further comprising:

a Wi-Fi sensor proximate to the particular air space, wherein the Wi-Fi sensor is configured to receive Wi-Fi signal data, the Wi-Fi signal data including data indicating a possible presence of a UAV within the particular air space; and

the processor further operatively coupled to the Wi-Fi sensor, the processor further operative to;

analyze the Wi-Fi signal data to determine a fourth confidence measure that the Wi-Fi signal data corresponds to a UAV; and

aggregate the fourth confidence measure into the combined confidence measure.

65. The system of claim 64, wherein the processor is further operative to:

extract a media access control (MAC) address from the Wi-Fi signal data;

compare the extracted MAC address to one or more known MAC addresses known to be associated with UAVs; and

;

upon determination that the extracted MAC address substantially matches at least one known MAC address, determine the fourth confidence measure.

66. The system of claim 64, wherein the processor is further operative to:

extract a service set identifier (SSID) from the Wi-Fi signal data;

compare the extracted SSID to one or more known SSIDs known to be associated with UAVs; and

upon determination that the extracted SSID substantially matches at least one known SSID, determining the fourth confidence measure.

67. The system of claim 64, wherein the processor is further operative to:

extract a received signal strength indicator (RSSI) from the Wi-Fi signal data; and

based on the extracted RSSI, estimate a physical distance of the object emanating the Wi-Fi signal data from the particular Wi-Fi sensor;

whereby the physical distance must be above a predetermined threshold distance value to indicate the presence of a UAV.

68. The system of claim 61, wherein the processor is further operative to:

identify at least one region of interest (ROI) in at least one video frame in the video data, the at least one ROI comprising the image of the object that may be a UAV flying within the particular air space; and

perform an object classification process with respect to the at least one ROI to determine whether the object in the image is a UAV, the object classification process comprising the steps of;

extracting image data from the image of the at least one ROI;

comparing the extracted image data to prior image data of objects known to be UAVs to determine a probability that the object in the image is a UAV; and

upon determination that the probability that the object in the image is a UAV exceeds a predetermined threshold, determining the first confidence measure.

69. The system of claim 61, wherein the processor is further operative to store in the database the video data and audio signal data in association with the indication that the UAV was identified in the particular air space.

70. The system of claim 61, wherein the processor is further operative to alert a system user that a UAV has been detected in the particular air space.

71. The system of claim 61, wherein the predetermined threshold value comprises a percentage.

72. The system of claim 61, wherein the video and audio sensor are enclosed in a unitary housing.