Systems and methods of non-line-of-sight passive detection and integrated early warning of an unmanned aerial system by a plurality of acoustic sensors are described. In some embodiments, the plurality of acoustic sensors is positioned within an intra-netted array in depth according to at least one of a terrain, terrain features, or man-made objects or structures. The acoustic sensors are capable of detecting and tracking unmanned aerial systems in non-line-of-sight environments. In some embodiments, the acoustic sensors may be in communication with internal electro-optical components or other external sensors, with orthogonal signal data then transmitted to remote observation stations for correlation, threat determination and if required, mitigation. The unmanned aerial systems may be classified by type and a threat level associated with the unmanned aerial system may be determined.

A multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle. The platform provides an automated tool that can integrate multi-modal sensor data including two-dimensional image data, three-dimensional image data, and motion, location, or orientation data, and create a visual representation of the integrated sensor data, in a live operational environment. An illustrative platform architecture incorporates modular domain-specific business analytics plug ins to provide real-time annotation of the visual representation with domain-specific markups.

A system and a method include an artificial intelligence control unit configured to receive data from notification sources. The data relate to an aircraft being operated by a pilot. The artificial intelligence control unit is further configured to determine relevant information for operating the aircraft from the data, and provide an information presentation including the relevant information on a display of a user interface of the aircraft. The aircraft is operated based on the relevant information.

A risk evaluation device includes an acquisition means and a risk evaluation means. The acquisition means acquires input information which includes location information indicating a location of a flying object, airframe information of the flying object, weather information, and environmental information. The risk evaluation means evaluates a risk in a case where the flying object is at the location, using a risk evaluation model which has been trained, based on the input information.

A risk evaluation device includes an acquisition means and a risk evaluation means. The acquisition means acquires input information which includes location information indicating a location of a flying object, airframe information of the flying object, weather information, and environmental information. The risk evaluation means evaluates a risk in a case where the flying object is at the location, using a risk evaluation model which has been trained, based on the input information.

A method for defending a predetermined area from an autonomously moving Unmanned Aerial Vehicle (UAV) is provided. The method includes generating one or more adversarial example adapted to disrupt a machine-learning based vision system of the UAV. Additionally, the method includes determining, based on geographical information about at least one of the predetermined area and a surrounding area of the predetermined area, a respective position for the one or more adversarial example in at least one of the predetermined area and the surrounding area of the predetermined area.

A method for defending a predetermined area from an autonomously moving Unmanned Aerial Vehicle (UAV) is provided. The method includes generating one or more adversarial example adapted to disrupt a machine-learning based vision system of the UAV. Additionally, the method includes determining, based on geographical information about at least one of the predetermined area and a surrounding area of the predetermined area, a respective position for the one or more adversarial example in at least one of the predetermined area and the surrounding area of the predetermined area.

Systems and methods for unmanned aerial vehicle (UAV) search and rescue operations are provided. A UAV may include a camera configured to capture one or more images of a search area. The UAV may include at least one logic device configured to calculate a flight pattern for searching the search area, receive a target image, and analyze the one or more images of the search area to identify a target matching the target image.

Systems and methods for unmanned aerial vehicle (UAV) search and rescue operations are provided. A UAV may include a camera configured to capture one or more images of a search area. The UAV may include at least one logic device configured to calculate a flight pattern for searching the search area, receive a target image, and analyze the one or more images of the search area to identify a target matching the target image.

Techniques for outputting landing area conditions to aircraft using standardized message protocols are provided. In an example method, a computing device accesses a configuration for generating messages using a predetermined message format for a first air traffic management device located at a first landing area. The computing device updates the configuration with information about a condition at the first landing area using the predetermined message format. The computing device outputs, using the first air traffic management device, a message based on the configuration, including transmitting the message to a second air traffic management device associated with an aircraft within a predetermined distance of the first landing area.