According to a method for predicting a call load of a controller, a flight trajectory of an aircraft is calculated by analyzing route information of an area to be predicted, a command intention of the controller and a flight intention of a pilot, and then a more accurate flight trajectory of the aircraft is predicted through the calculated flight trajectory, so that a future call node and content are acquired. The predicted call content is combined with the current specific control scene to predict call time required by the call content. Finally, the call load is calculated by the required call content and time and the call node, finally the purpose of predicting the call load of the controller in a time period of the future is achieved, and more reliable support is provided for timing requirements in an air traffic control process.

According to a method for predicting a call load of a controller, a flight trajectory of an aircraft is calculated by analyzing route information of an area to be predicted, a command intention of the controller and a flight intention of a pilot, and then a more accurate flight trajectory of the aircraft is predicted through the calculated flight trajectory, so that a future call node and content are acquired. The predicted call content is combined with the current specific control scene to predict call time required by the call content. Finally, the call load is calculated by the required call content and time and the call node, finally the purpose of predicting the call load of the controller in a time period of the future is achieved, and more reliable support is provided for timing requirements in an air traffic control process.

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. A tracking system can include one or more first computing devices that receive sensor data associated with an object in a particular airspace from one or more sensors. The first computing device can analyze the sensor data relating to the object to determine information about the object. A portable countermeasure device can include one or more second computing devices. The second computing device can receive the information relating to the object. The second computing device can display a visual indicator indicating the information on a display.

A method of setting an initial location of an aerial vehicle for the replacement of the aerial vehicle of a mobile backhaul system includes obtaining a first separation distance between a mobile backhaul hub and a first aerial vehicle, obtaining a second separation distance between a second aerial vehicle and the mobile backhaul hub by considering the location of the first aerial vehicle, a service area radius of a first flying base station mounted on the first aerial vehicle, and information on a handover overlap area in which handover to the second aerial vehicle is considered, calculating an angle of interference for 3-D rotation transformation based on the first separation distance and the second separation distance, and calculating location information of the second aerial vehicle to be replaced based on coordinate transformation and 3-D rotation transformation with respect to the location information of the first aerial vehicle.

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.

Systems, methods, and devices for automatic signal detection in an RF environment are disclosed. A sensor device in a nodal network comprises at least one RF receiver, a generator engine, and an analyzer engine. The at least one RF receiver measures power levels in the RF environment and generates FFT data based on power level data. The generator engine calculates a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of the FFT data. The analyzer engine creates a baseline based on statistical calculations of the power levels measured in the RF environment for a predetermined period of time, and identifies at least one signal based on the first derivative and the second derivative of the FFT data in at least one conflict situation from comparing live power distribution to the baseline of the RF environment.

An airspace flow prediction method based on an ensemble learning algorithm is provided, which includes the steps: collecting historical airspace flow data and related spatial structure data, and preprocessing; constructing a GNN model, and calculating an influence degree of each node and an influence degree between the nodes in an airspace network by using the GNN model, the node being any airport or any waypoint; performing, by the GNN model, feature conversion and attention fusion on the influence degree of the node, the influence degree between the nodes and time series data to acquire a fused feature vector; and inputting the fused feature vector into an LSTM model to acquire a predicted airspace flow of the node.

The present application relates to a method and an apparatus for path reporting. One embodiment of the subject application provides a method performed by a primary user equipment (UE) in a UE group, comprising: receiving one or more secondary path reports, each including a secondary path of a corresponding secondary UE in the UE group; determining a group path report based on a primary path of the primary UE and/or the one or more secondary paths; and transmitting the group path report to a Base Station (BS).

Methods, systems, and devices for wireless communications are described. A wireless network may receive a message from a wireless device that is coupled with an aerial vehicle. The message may include a network identifier, an aerial identifier, and operational information for the aerial vehicle. The wireless network may send a message to an aerial function management system requesting that the aerial function management system authenticate an identity of the aerial vehicle. The wireless network may also request that the aerial function management system approve a flight path for the aerial vehicle. The wireless network may establish a data session with the wireless device based on an authentication of the aerial vehicle and an approval of the flight path.

A system and method for identifying slow-moving and smaller flying objects using one or more radar based sensors is provided. In one or more examples, a radar system can be configured to generate plot data corresponding to flying objects in a given airspace. A tracker can be configured to receive the plot data, and can be configured to generate one or more tracks. The one or more tracks generated by the tracker can then be inputted into a classifier that is configured to distinguish unmanned aerial vehicle (UAV) traffic from birds that are flying in the airspace. In one or more examples, the classifier can generate an N-dimensional hypercube, with each dimension of the hypercube pertaining to a specific attribute of the flying objects. Each track can be converted into a data point within the hypercube and the data points can be clustered to determine whether the track belongs to a bird or a UAV.