A flight planning system for providing a flight planning tool comprises a memory device for storing flight information and one or more hardware processors configured to: receive a flight plan, access a calendar of events, determine events corresponding to dates associated with the flight plan, and provide a notification of the events on an interactive user interface.

A flight task processing method includes generating and displaying a user prompt according to flight data of a plurality of flight tasks, selecting one of the flight tasks as a target flight task in response to a selection operation with respect to the user prompt, determining the flight data of the target flight task, processing the flight data of the target flight task to obtain control instruction, and automatically controlling an operation of an aerial vehicle according to the control instruction to reproduce the target flight task by controlling the aerial vehicle to fly to a waypoint included in the flight data, controlling a gimbal of the aerial vehicle to face a gimbal orientation included in the flight data while the aerial vehicle is at the waypoint, and controlling a camera carried by the gimbal to acquire an image while the aerial vehicle is at the waypoint.

Autonomous unmanned vehicles (UVs) for responding to situations are described. Embodiments include UVs that launch upon detection of a situation, operate in the area of the situation, and collect and send information about the situation. The UVs may launch from a vehicle involved in the situation, a vehicle responding to the situation, or from a fixed station. In other embodiments, the UVs also provide communications relays to the situation and may facilitate access to the situation by responders. The UVs further may act as decoupled sensors for vehicles. In still other embodiments, the collected information may be used to recreate the situation as it happened.

A method of optimizing the operation of a fleet of gas turbine engines is provided. The method comprises the steps of: (a) measuring respective values for plural control actuator settings within each of the gas turbine engines; (b) deriving, based on data external to the operation of the gas turbine engines, a desired performance modification of the gas turbine engines; (c) determining, based on the measured control actuator settings, one or more respective trim signals for varying selected of the control actuator settings to achieve the desired performance modification; and (d) transmitting the trim signals to respective electronic controllers of the engines to vary the selected control actuator settings accordingly.

The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

In one implementation, first and second messages are received that include encoded position information for a transmitter. It is determined that both were received within some time of a previous message and that the second message was received within some time of the first message. A first location of the transmitter is determined based on the encoded position in the first message and the previously determined location. A second location of the transmitter is determined based on the encoded position in the second message and the previously determined location. It also is determined that the first and second locations are within a threshold distance. An updated second location of the transmitter is determined based on the encoded position information in the second message and the first location. A determination is made that the second location and the updated second location are within a threshold distance.

A gateway retrieval alert device is configured for use in an aircraft ground support system. The gateway retrieval alert device is connected to an intercom gateway, either through a direct wireless connection or through a direct connection to a headset of a tug operator, the headset in turn being wirelessly connected to the intercom gateway. The integrity of the wireless link to the intercom gateway is monitored, and when the integrity of the link falls below a set threshold, an alert is issued. The alert is issued externally to the communication network between the ground crew and pilot.

A method of targeting, which involves capturing a first video of a scene about a potential targeting coordinate by a first video sensor on a first aircraft; transmitting the first video and associated potential targeting coordinate by the first aircraft; receiving the first video on a first display in communication with a processor, the processor also receiving the potential targeting coordinate; selecting the potential targeting coordinate to be an actual targeting coordinate for a second aircraft in response to viewing the first video on the first display; and guiding a second aircraft toward the actual targeting coordinate; where positive identification of a target corresponding to the actual targeting coordinate is maintained from selection of the actual targeting coordinate.

In some examples, a system includes a memory configured to store a buffer storing volumetric weather data. The system also includes processing circuitry configured to retrieve weather radar data from the buffer by retrieving a first reflectivity value associated with a first cell of the buffer, where the first cell represents a first volume of space. The processing circuitry is further configured to determine an altitude of a first location based on a parameter and an altitude extent of the first volume of space. The altitude of the first location is lower than an altitude of a centroid of the first volume of space. The processing circuitry is also configured to associate the first reflectivity value with the first location.

An aircraft control system (100) including an input interface (102), an output interface (114) and a processing engine (108) having a classifier (110) that applies input data (104) generated by the input interface (102) to generate output control data (112). The classifier (110) has a plurality of parameters which represent a control policy for operating the aircraft (800). The output interface (114) generates control outputs to control the aircraft (800) based on the output control data (112). A machine learning system (900) for training the classifier (110) including an environment (902), a pathway evaluation engine (904), storage (906), and a training engine (908). The machine learning system (900) generates training data (912) by selecting a pathway representing an operating procedure using the pathway evaluation engine (904). The training engine (908) trains the classifier (110) using the training data (912).