Dedrone Holdings, Inc.

Rene Seeber

Kai Baumgart

Henning Meyer

David Prantl

Markus Schoeler

Christian Scheibe

Ingo Seebach

Alexander Morrow

Zachary Schmid

Joe Price

Mitch Meverden

Florian Hoidn

Michael Dyballa

Frederic Beister

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. A tracking system can receive sensor data associated with an object in a particular airspace from one or more radio frequency sensors. The tracking system can analyze the sensor data relating to the object to identify a type of RF signal being used by the object. A portable countermeasure device can generate one or more disruption signals on one or more targeted bands of spectrum based on the type of RF signal being used by the object.

Identifying, tracking, and disrupting unmanned aerial vehicles

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. A tracking system can include one or more first computing devices that receive sensor data associated with an object in a particular airspace from one or more sensors. The first computing device can analyze the sensor data relating to the object to determine information about the object. A portable countermeasure device can include one or more second computing devices. The second computing device can receive the information relating to the object. The second computing device can display a visual indicator indicating the information on a display.

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. A computing device can tune the RF receiver to a particular frequency set. The computing device can receive RF signal data corresponding to a plurality of RF signals via the RF receiver. The computing device can detect a plurality of signal characteristics corresponding to the plurality of RF signals from the RF signal data. The computing device can identify a matching RF signal by comparing the RF signal data to a plurality of known RF signals. The computing device can apply a predetermined rule set to the matching RF signal to determine at least one action to take.

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.

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. Radar sensors included in the plurality of sensors may identify specific locations corresponding to objects in an airspace, and video sensors such as PTZ cameras may be directed to collect image data of the objects at the specific locations for identifying the objects as UAVs and tracking the UAVs. The system predicts next locations for the UAVs and directs the PTZ cameras to update their orientations for following the UAVs.

Systems, methods, apparatuses, and devices for radar-based identifying, tracking, and managing of unmanned aerial vehicles

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. Sensor data can be received from one or more portable countermeasure devices or sensors. The sensor data can relate to an object detected proximate to a particular airspace. The system can analyze the sensor data relating to the object to determine a location of the object and determine that the object is flying within the particular airspace based at least in part on location data. A portable countermeasure device can be identified that corresponds to the location of the object. The system can transmit information about the object to the identified portable countermeasure device. The portable countermeasure device can transmit additional data relating to the object to the system.

A handheld, man-portable countermeasure device can include a directional antenna and signal disruption components in electronic communication with the directional antenna. The signal disruption components can include a signal generator coupled to an amplifier. The countermeasure device can include a computing device coupled to an electronic communications transceiver. The computing device can provide, via the electronic communications transceiver, at least one bilateral communication link to operatively connect with a UAV tracking system. The computing device can determine status information associated with the electronic circuitry of the countermeasure device. The computing device can transmit, via the bilateral communication link, the status information to the UAV tracking system.

Systems, methods, apparatuses, and devices for identifying, tracking, and deterring UAVs via ADS-B signals are disclosed. The system leverages a plurality of sensors operatively connected over a network to a configuration of software and/or hardware for detecting approaching UAVs. In response to detecting approaching UAVs, the system generates and propagates ADS-B signals to be received by the UAVs for deterring the UAVs from entering a particular airspace, wherein the particular airspace is indicated by data encoded within the ADS-B signal. According to particular aspects of the present disclosure, the ADS-B signals can be propagated continuously or on-demand. Furthermore, the ADS-B signals may be generated based on real-time GPS data, or the ADS-B signals may be generated based on 'spoofed' or hard-coded location data, for disguising a particular ADS-B signal as being transmitted from a different location.

Systems, methods, apparatuses, and devices for identifying, tracking, and deterring unmanned aerial vehicles via ADS-B signals

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.

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Kassel, Germany

Rene Seeber