Dedrone Holdings, Inc.

Cisco Technology, Inc.

David Prantl

Rene Seeber

Kai Baumgart

Henning Meyer

Markus Schoeler

Christian Scheibe

Ingo Seebach

Carlos M Pignataro

Gonzalo A Salgueiro

Ammar Rayes

Hazim Hashim Dahir

Rajesh Vargheese

James D Stanley, Iii

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. 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.

A plurality of data sets are compiled in a memory. A first data set comprises a location mapping of an item of interest within a predetermined environment, and a second set comprises energy usage data within the predetermined environment. The first data set is correlated, via a processor, with the second data set to locate a subset of energy usage data in the second data set associated with the item of interest. A third data set is generated by combining the first data set with the subset of energy usage data in the second data set to increase an accuracy of the location mapping of the item of interest based upon the subset of energy usage data. The third data set is applied to a computing resource.

Time and motion data fusion for high fidelity data sets

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

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Hamburg, Germany

David Prantl