A computer-implementable method employs radio signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one radio communication protocol. The inferred function is used to map metadata of a detected radio signal to a cognitive profile of a transmitter of the detected radio signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected radio signal. A response to the transmitter is based upon the mapping.

A system for providing integrated detection and deterrence against an unmanned vehicle including but not limited to aerial technology unmanned systems using a detection element, a tracking element, an identification element and an interdiction or deterrent element. Elements contain sensors that observe real time quantifiable data regarding the object of interest to create an assessment of risk or threat to a protected area of interest. This assessment may be based e.g., on data mining of internal and external data sources. The deterrent element selects from a variable menu of possible deterrent actions. Though designed for autonomous action, a Human in the Loop may override the automated system solutions.

Systems and methods for UAV safety are provided. An authentication system may be used to confirm UAV and/or user identity and provide secured communications between users and UAVs. The UAVs may operate in accordance with a set of flight regulations. The set of flight regulations may be associated with a geo-fencing device in the vicinity of the UAV.

A portable countermeasure device is provided comprising one or more directional antennae, one or more disruption components and at least one activator. The portable countermeasure device further comprises a body having a dual-grip configuration, with the directional antennae are affixed to a removable plate on a front portion of the body. The one or more disruption components may be internally mounted within the device body. The dual-grip configuration allows an operator to use his body to steady and support the device while maintaining the antenna on target. The second grip is positioned adjacent the first grip, with the first grip angled toward the rear of the device and the second grip angled toward the front of the device. The portable countermeasure device is aimed at a specific drone, the activator is engaged, and disruptive signals are directed toward the drone, disrupting the control, navigation, and other signals to and from the drone.

A drone defense system (DDS) beacon detects unmanned aerial systems (UAS) traffic and transmits a broadcast signal over a transmission region indicating a no-fly zone in which only UAS having an authorization are allowed to fly. The beacon allows UAS with clearance to enter the no-fly zone. Those UAS without clearance are diverted around the no-fly zone, denied Wi-Fi and/or RF connection, forced to return to home launch sites via activation of standard preprogrammed Return to Home (RTH) routines, or forced to land at specified locations where they may be captured. Military, emergency medical services (EMS), and other UAS are allowed to enter no-fly zones in which other UAS, such as commercial, or consumer UAS, cannot enter. DDS cloud collects and stores log data from all deployed DDS beacons. DDS cloud can send system software updates to DDS beacons, make real-time statistical analysis, and provide report data to outside systems.

A system for providing integrated detection and deterrence against an unmanned vehicle including but not limited to aerial technology unmanned systems using a detection element, a tracking element, an identification element and an interdiction or deterrent element. Elements contain sensors that observe real time quantifiable data regarding the object of interest to create an assessment of risk or threat to a protected area of interest. This assessment may be based e.g., on data mining of internal and external data sources. The deterrent element selects from a variable menu of possible deterrent actions. Though designed for autonomous action, a Human in the Loop may override the automated system solutions.

Unauthorized operation of a UAV may present privacy or security risks. A software-defined radio (SDR) or other receiver can be used to monitor a specified range of frequencies to provide detection of wireless communication signals suspected of relating to UAV operation. A protocol detector corresponding to a trained classifier can be applied to data packets demodulated by the SDR. A transmitter can then be triggered to provide warnings by injecting warning data into a video channel in response to the detected protocol. Control of the UAV can be established by transmitting simulated control commands that overwhelm the signals received from the UAVs normal remote control. If transmission of warnings or simulated control signals fail to suppress unwanted UAV operation, other actions can be triggered such as jamming or dispatch of an interceptor such as a surveillance UAV.

The system and methods described herein aids in the defense of unmanned vehicles, such as aerial vehicles, from wifi cyber attacks. Such attacks usually do not last long and in the case of many point-to-point command and control systems, the attacks originate from close proximity to the unmanned vehicle. The system and methods described herein allow a team to rapidly identify and physically respond to an adversary trying to take control of the unmanned vehicle. Another aspect of the embodiment taught herein is to allow for the location of a wifi signal in a hands-free manner by able to visualize the source of the signal using an augmented reality display coupled to an antenna array.

A computer-implementable method for generating a cognitive insight is performed by a counter-unmanned autonomous vehicle (UAV) system. The method comprises receiving training data based upon sensor measurements of at least one UAV for processing in a cognitive learning and inference system. The system performs a plurality of machine learning operations on the training data to generate a cognitive profile of the at least one UAV. A cognitive insight is generated based upon the cognitive profile, and a countermeasure is enacted against the UAV based upon the cognitive insight.

A system for detecting and defeating a drone is disclosed which includes a detection antenna array configured to detect the drone and a control signal of the drone in a 360 degree field, the detection antenna array being further configured to detect the directionality of the drone with reference to the most dominant of the control signal of the drone detected by each of a plurality of antennas within the detection antenna array; a neutralization system disposed in communication with the detection antenna array; the neutralization system including a transmission antenna configured to transmit an override signal to the detected drone, an amplifier configured to modulate a gain of the override signal, and a processing device configured to generate the override signal and control transmission of the override signal.