Systems, methods and apparatus are described for a HERF weapon that may emit high-energy radio waves at a target based on locational information and a frequency associated with the target. The HERF weapon may receive the frequency and locational information from a sensing system. The HERF weapon may emit a high energy pulse toward the target and on the frequency associated with the target to disable or destroy the target without affecting nearby devices. The HERF weapon may allow the user to avoid detection by using a frequency that corresponds to the target's operating frequency.

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.

Systems and methods are described for a cyber-physical vehicle management system generated by an Integrated Secure Device Manager (ISDM) Authority configured to manage licensing and approval of Cyber-Physical Vehicle (CPV)s, a public/private key pair and a unique ID for the Authority, create a self-signed Authority token signed by the private key, send the Authority token to a plurality of ISDM Node device configured to verify Module device authenticity and in communication with the Authority, store, by each Node, the Authority token, and mark, by each Node, the Authority token as trusted.

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.

The present disclosure relates to a counter measure effector (100) for targeting unmanned aerial vehicles (UAVs), said counter measure effector comprising: at least one antenna (108, 109) for selectively emitting electromagnetic radiation; a telescopic sight (126) comprising an optical system that is transferrable between a first state, in which the optical system has a first appearance, and a second state, in which the optical system has a second appearance that is different from the first appearance, wherein the counter measure effector (100) is configured to set the optical system in its first state, when the at least one antenna is activated, and in its second state, when the at least one antenna is de-activated.

An Unmanned Aerial Vehicle is disclosed. The Unmanned Aerial Vehicle includes a body, rotors attached to the body, one or more sensors, and an electromagnetic pulse transmitter. The electromagnetic pulse transmitter is configured to transmit an EMP and the Unmanned Aerial Vehicle is configured to track a target Unmanned Aerial Vehicle using the one or more sensors and direct the electromagnetic pulse transmitter at the target Unmanned Aerial Vehicle to disrupt the target Unmanned Aerial Vehicle.

Method and apparatus for cooperative early threat detection and avoidance in C-V2X. In one aspect, the apparatus detects a threat entity within a threat zone based on data signals received from the threat entity, wherein the threat entity obstructs wireless spectrum or resources utilized in cooperative or automated driving decisions. The apparatus transmits, to at least one second wireless device, a message indicating the threat entity within the threat zone.

A system, method and computer program product for controlled drone descent, and deactivation, including a drone deactivation system; and a location system. The drone deactivation system calculates positioning, signal reception, signal strength, and signal identification parameters of a target drone from the location system, and determines an attack method based on the calculated parameters. The drone deactivation system employs the determined attack method against the target drone for forcing at least one of controlled drone descent, and deactivation of the target drone.

An internet of things is disclosed, comprising plural SDR receivers and possibly a centralised system, where one or more of the receivers may be mobile. The internet of things thus allows for a very large proportion of RF signals present within a city, for example, to be monitored and analysed for the purpose of identifying, tracking and/or preventing criminal behaviour. The receivers may be equipped with secure SDRs for increased security and privacy and the system preferably includes artificial intelligence using machine learning technology, for increased adaptability among others. The system is flexible due to the programmability of the SDRs.

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.