SYSTEMS AND APPARATUSES FOR DETECTING UNMANNED AERIAL VEHICLE

• US 20200100065A1
• Filed: 03/22/2017
• Published: 03/26/2020
• Est. Priority Date: 03/22/2017
• Status: Active Grant

• Current Assignee: Nokia Technologies Oy (Nokia Corporation)
• Original Assignee: Nokia Technologies Oy (Nokia Corporation)
• Inventors: LIANG, Yuyang
• Granted Patent: US 10,735,908 B2
• Time in Patent Office: Days
• CPC Class Codes: G01S 2205/03 Airborne
• CPC Class Codes: G01S 5/0036 of measured values, i.e. measurement on mobile and position calculation on base station
• CPC Class Codes: G01S 5/01 Determining conditions which influence positioning, e.g. radio environment, state of motion or energy consumption
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
• CPC Class Codes: G01S 5/0295 Proximity-based methods, e.g. position inferred from reception of particular signals
• CPC Class Codes: G01S 5/18 using ultrasonic, sonic, or infrasonic waves
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G08B 13/1672 using sonic detecting means, e.g. a microphone operating in the audio frequency range
• CPC Class Codes: G08B 13/181 using active radiation detection systems
• CPC Class Codes: G08B 21/182 Level alarms, e.g. alarms responsive to variables exceeding a threshold
• CPC Class Codes: G08B 23/00 Alarms responsive to unspecified undesired or abnormal conditions
• CPC Class Codes: G08B 7/06 using electric transmission , e.g. involving audible and visible signalling through the use of sound and light sources
• CPC Class Codes: G10L 25/51 for comparison or discrimination
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 8/22 Processing or transfer of terminal data, e.g. status or physical capabilities
• CPC Class Codes: H04W 84/06 Airborne or Satellite Networks space-based or airborne stations H04B7/185

Abstract

Apparatuses, methods, and computer program products disclosed herein provide improved unmanned aerial vehicle (UAV) detection. A method may include receiving data including wireless signal strength collected by one or more devices, monitoring the data including the wireless signal strength to determine if the wireless signal strength of a wireless signal source increases over time in a manner to satisfy a predefined threshold in order to be indicative of a UAV, determining a trajectory of the UAV based upon the data including the wireless signal strength, and generating an alert based on, at least, the trajectory of the UAV as indicated by the data including the wireless signal strength.

Claims

1. A method comprising:

receiving data including wireless signal strength collected by one or more devices;

monitoring the data including the wireless signal strength to determine if the wireless signal strength of a wireless signal source increases over time in a manner to satisfy a predefined threshold in order to be indicative of an unmanned aerial vehicle (UAV);

determining a trajectory of the UAV based upon the data including the wireless signal strength; and

generating an alert based on, at least, the trajectory of the UAV as indicated by the data including the wireless signal strength.

2. The method of claim 1, further comprising:

receiving an audio noise signal;

determining whether the audio noise signal is associated with the UAV based on signal strength; and

generating another alert in an instance in which the audio noise signal is associated with the UAV.

3. The method according to claim 1, wherein determining the audio noise signal is associated with the UAV comprises analyzing an audio noise strength of the received audio noise signal and identifying the UAV in an instance in which the audio noise signal satisfies a predetermined threshold.

4. The method according to claim 1, further comprising determining an estimated time of arrival of the UAV based on the data including the wireless signal strength.

5. The method of claim 4 wherein determining the estimated arrival time further comprises calculating the estimated arrival time based upon the trajectory and timestamp information associated with the data collected by one or more devices.

6. The method according to claim 1, further comprising:

maintaining an address dictionary of address of a plurality of wireless sources; and

comparing address information from the data collected to the addresses maintained by the address dictionary to facilitate a determination of the UAV.

7. The method according to claim 1, wherein the data includes position data that is based on a position of the one or more devices of a crowdsourcing network.

8. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory coupled to the at least one processor, and the computer program code configured to, when executed by a processor, cause the apparatus to:

receive data including wireless signal strength collected by one or more devices;

monitor the data including the wireless signal strength to determine if the wireless signal strength of a wireless signal source increases over time in a manner to satisfy a predefined threshold in order to be indicative of an unmanned aerial vehicle (UAV);

determine a trajectory of the UAV based upon the data including the wireless signal strength; and

generate an alert based on, at least, the trajectory of the UAV as indicated by the data including the wireless signal strength.

9. The apparatus of claim 8, wherein the apparatus is further caused to:

receive an audio noise signal;

determine whether the audio noise signal is associated with the UAV based on signal strength; and

generate another alert in an instance in which the audio noise signal is associated with the UAV.

10. The apparatus according to claim 8, wherein determining the audio noise signal is associated with the UAV comprises analyzing an audio noise strength of the received audio noise signal and identifying the UAV in an instance in which the audio noise signal satisfies a predetermined threshold.

11. The apparatus according to claim 8, wherein the apparatus is further caused to determine an estimated time of arrival of the UAV based on the data including the wireless signal strength.

12. The apparatus of claim 11 wherein determining the estimated arrival time further comprises calculating the estimated arrival time based upon the trajectory and timestamp information associated with the data collected by one or more devices.

13. The apparatus according to claim 8, wherein the apparatus is further caused to:

maintain an address dictionary of address of a plurality of wireless sources; and

compare address information from the data collected to the addresses maintained by the address dictionary to facilitate a determination of the UAV.

14. The apparatus according to claim 8, wherein the data includes position data that is based on a position of the one or more devices of a crowdsourcing network.

15. A computer program product comprising a non-transitory computer readable storage medium, the non-transitory computer readable storage medium comprising instructions that, when executed by a device, configure the device to:

receive data including wireless signal strength collected by one or more devices;

monitor the data including the wireless signal strength to determine if the wireless signal strength of a wireless signal source increases over time in a manner to satisfy a predefined threshold in order to be indicative of an unmanned aerial vehicle (UAV);

determine a trajectory of the UAV based upon the data including the wireless signal strength; and

generate an alert based on, at least, the trajectory of the UAV as indicated by the data including the wireless signal strength.

16. The computer program product of claim 15, wherein the device is further configured to:

receive an audio noise signal;

determine whether the audio noise signal is associated with the UAV based on signal strength; and

generate another alert in an instance in which the audio noise signal is associated with the UAV.

17. The computer program product according to claim 15, wherein determining the audio noise signal is associated with the UAV comprises analyzing an audio noise strength of the received noise signal and identifying the UAV in an instance in which the audio noise signal satisfies a predetermined threshold.

18. The computer program product according to claim 15, wherein the device is further configured to determine an estimated time of arrival of the UAV based on the data including the wireless signal strength.

19. The computer program product of claim 18 wherein determining the estimated arrival time further comprises calculating the estimated arrival time based upon the trajectory and timestamp information associated with the data collected by one or more devices.

20. The computer program product according to claim 15, wherein the device is further configured to:

maintain an address dictionary of address of a plurality of wireless sources; and

compare address information from the data collected to the addresses maintained by the address dictionary to facilitate a determination of the UAV.

21. The computer program product according to claim 15, wherein the data includes position data that is based on a position of the one or more devices of a crowdsourcing network.

22-28. -28. (canceled)