The present disclosure realizes a configuration capable of accurately displaying a flight path of a drone on an actually captured image of the drone. The configuration includes a data processing unit that displays a moving path of a moving device such as a drone on a display unit that displays a camera-capturing image of the moving device. The data processing unit generates a coordinate conversion matrix for performing coordinate conversion processing of converting position information according to a first coordinate system, for example, the NED coordinate system indicating the moving path of the moving device into a second coordinate system, for example, the camera coordinate system capable of specifying a pixel position of a display image on the display unit and outputs, to the display unit, the moving path having position information according to the camera coordinate system generated by coordinate conversion processing to which the generated coordinate conversion matrix is applied.

A drone classification device is provided. The drone classification device includes a radio signal receiver configured to receive a radio signal, and a radio signal analyzer configured to determine physical characteristics of the received radio signal, to compare the determined physical characteristics of the received radio signal with a plurality of reference characteristics, each reference characteristics describing a drone class of a plurality of drone classes, and to classify a drone into a drone class of a plurality of drone classes depending on a result of the comparison.

Training an encoder is provided. The method comprises inputting a current state of a number of aircraft into a recurrent layer of a neural network, wherein the current state comprises a reduced state in which a value of a specified parameter is missing. An action applied to the aircraft is input into the recurrent layer concurrently with the current state. The recurrent layer learns a value for the parameter missing from current state, and the output of the recurrent layer is input into a number of fully connected hidden layers. The hidden layers, according to the current state, learned value, and current action, determine a residual output that comprises an incremental difference in the state of the aircraft resulting from the current action.

An embodiment method for controlling operation of an aerial vehicle in an aerial vehicle control system includes approving entry of the aerial vehicle into an aerial vehicle operation zone from a take-off and landing facility of a departure location built into the aerial vehicle control system, controlling an operation of the aerial vehicle in the aerial vehicle operation zone, and approving exit of the aerial vehicle from the aerial vehicle operation zone into a take-off and landing facility of a destination location.

A method for determining an optimized trajectory to be followed by an aircraft, the method being implemented by a determining system of the aircraft and comprising: a first step for acquiring at least one constraint relative to at least one parameter of the trajectory to be followed, the constraint being determined by a control system of a remote station as a function of the air traffic and/or the aircraft mission; a step for calculating a desired trajectory as a function of the constraint; a step for transmitting the desired trajectory to the remote station; a second step for acquiring an instruction comprising an authorization to follow the desired trajectory or a refusal of the desired trajectory.

A mobile tracking system including an antenna, gimbal, and GPS subsystem. The mobile tracking system is operable with a plurality of models of gimbal and can automatically determine gimbal parameters based upon a detected model. This allows for plug and play of several gimbal models without the need for further input provided by a user. The mobile tracking system can also identify positional information for the system itself as well as for a tracked node, and can provide gimbal pan/tilt instructions based upon both. This allows for accurate tracking in an environment where the MTS itself is moving.

Devices and methods implemented by a computer for connecting between a human-machine interface (e.g. graphical), supplied with data, in particular traffic and meteorology data, and a system for computing paths (e.g. avionic flight management), for interactive exploration of usable flight paths for managing the spatial congestion around the path of a vehicle are provided. Developments describe the particular case of an aircraft, such as mission management, the gathering of spatial, temporal or technical constraints relating to the spatial congestion within the determined potential airspace, the monitoring of changes in the spatial congestion of the airspace, excursions of the aircraft, miscellaneous displays, and n particular superimposed displays. Various types of human-machine interfaces are described, involving virtual or augmented reality and being configured to display data in 2D, 3D and/or 4D.

A method and a device for determining emergency trajectories. A method and a device for operating an automated vehicle are also described.

A communication system includes a traffic relay (TR) device for locating unmanned traffic in a controlled airspace. The TR device comprises one or more processors configured to perform the step of receive a status message from an unmanned aerial vehicle (UAV). The status message provides identification and location information of the UAV. The one or more processors are also configured to perform the step of generate a warning message for communication to at least one manned aerial vehicle (MAV) or a manned traffic manager (MTM) device that is communicatively connected to the at least one MAV. The warning message is based on a comparison of the location of the UAV to a designated airspace.

A flight pushback state monitoring method based on multi-modal data fusion comprises: 1, constructing a control intention recognition rule, and recognizing a pushback intention from a control instruction sent by a controller; 2, constructing a flight intention recognition model, extracting an aircraft action from a real-time monitoring video, and capturing a flight intention; and 3, constructing an intention alignment fusion rule, and judging whether control intention information conflicts with flight intention information; by fusing the control intention and the flight intention, the method can realize the following auxiliary functions: timely judging whether the aircraft follows the pushback instruction sent by the controller, if a captain does not act according to the control instruction or acts arbitrarily without a control instruction, giving an inconsistent alarm, and a function of monitoring the flight pushback state is implemented.