Systems, methods, and devices are provided for controlling an unmanned aerial vehicle (UAV) associated with flight response measures. The flight response measure may be generated by assessing one or more flight-restriction strips, assessing at least one of a location or a movement characteristic of the UAV relative to the one or more flight-restriction strips, and directing, with aid of one or more processors, the UAV to take one or more flight response measures based on at least one of the location or movement characteristic of the UAV relative to the one or more flight-restriction strips.

Systems, methods, and apparatus for identifying and tracking UAVs including a computing device and a Wi-Fi sensor. The computing device can receive Wi-Fi data from the Wi-Fi sensor comprising an RSSI and a MAC address. The computing device can determine an estimated proximity of an unmanned aerial vehicle (UAV) based on the RSSI. The computing device can compare the estimated proximity to a signal threshold. The computing device can determine whether the MAC address matches one of a plurality of known UAV MAC addresses. The computing device can apply rule set to determine an action to take. The computing device can perform the action.

Systems, methods, and apparatus for identifying and tracking UAVs including an image capturing device. A computing device can receive a frame captured via an image capturing device configured to monitor a particular air space. The computing device can identify a region of interest (ROI) in the frame. The ROI can include an image of an object. The computing device can perform a background subtraction process on the frame. The computing device can scale the frame to a uniform size. The computing device can perform a comparison of the frame to reference images. The reference images can include known unmanned aerial vehicle (UAV) images and known non-UAV images. The computing device can classify the object with a UAV classification based on the comparison.

A computing device to determine engine thrust of an aircraft during taxiing. The computing device includes memory circuitry configured to store computer-readable program code. Processing circuitry is configured to execute the computer-readable program code to cause the computing device to: identify an aircraft on an airport surface; calculate an acceleration of the aircraft during the taxiing of the aircraft; compare the acceleration with predetermined data from similar aircraft; and based on the comparison, determine an engine thrust for the aircraft while the aircraft is taxiing.

A system integrates active radar detection and passive RF signal spectrum analysis to produce an enhanced situational awareness dataset, enabling advanced transportation management and unmanned aerial systems (UAS) air traffic deconfliction. By correlating radar detections with ambient RF emissions, the system provides improved target identification, reduces false positives, and supports real-time autonomous decision-making for airspace safety and regulatory compliance.

A system integrates active radar detection and passive RF signal spectrum analysis to produce an enhanced situational awareness dataset, enabling advanced transportation management and unmanned aerial systems (UAS) air traffic deconfliction. By correlating radar detections with ambient RF emissions, the system provides improved target identification, reduces false positives, and supports real-time autonomous decision-making for airspace safety and regulatory compliance.

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.

A system may include a processor configured to: (a) obtain parameters; (b) based on the parameters, update flight-state data associated with an aircraft; (c) obtain a trained machine learning (ML) model; (d) based at least on the updated flight-state data and the trained ML model, infer a direction from a current cell for a reroute; (e) based on the inferred direction and the updated flight-state data, set the current cell and identify neighboring cells; (f) calculate an optimal next cell by using a shortest path finding (SPF) algorithm to select the optimal next cell from the neighboring cells; (g) iteratively repeat steps (d) through (f) such that the current cell is set as the optimal next cell until a goal state is reached; (h) construct a re-route using optimal cells iteratively calculated in step (f); and (i) output the re-route.

Methods, devices, and systems for false target detection for airport traffic control are described herein. One device includes a user interface, a memory, and a processor configured to execute executable instructions stored in the memory to receive one or more sensor reports from one or more sensors, aggregate data that corresponds to a particular target from the one or more sensor reports, determine the particular target is a false target responsive to only one of the sensor reports including data that corresponds to the particular target, and display the particular target as a false target on the user interface responsive to determining the particular target is a false target.