Disclosed are methods, systems, and non-transitory computer-readable media for transmitting obstacle information to one or more operator displays associated with an aircraft. For instance, the method may include obtaining aircraft flight information including a current position and altitude; retrieving obstacle information for a flight area; scanning the flight area with environment sensors to identify observed obstacles and generate observed obstacle information. The method may further include aggregating the obstacle information with the observed obstacle information of the observed obstacles identified by the environment sensors to generate aggregated obstacle information identifying obstacles in the flight area; determining obstacle characteristics of the obstacles located in the flight area; assigning visual characteristics to each of the obstacles based at least in part on the determined obstacle characteristics; determining a subset of the obstacles relevant to the aircraft; and transmitting information on the relevant subset of obstacles to a display of the aircraft.

A method of deploying an unmanned aerial vehicle (UAV) operation system may be provided. A method may include estimating an amount of traffic for one or more routes based on a demand of the one or more routes. The method may also include determining a required number of docking stations for each route of the one or more routes based on the estimated amount of traffic for the route, a distance of the route, and a maximum travel distance for a UAV. Further, the method may include installing the required number of docking stations for each route of the one or more routes, wherein each docking station of the required number of docking stations including at least one of a power supply, a wireless charger, a communication module, a control module, and a camera.

A terrestrial acoustic sensor array for detecting and preventing airspace collision with an unmanned aerial vehicle (UAV) includes a plurality of ground-based acoustic sensor installations, each of the acoustic sensor installations including a sub-array of microphones. The terrestrial acoustic sensor array may further include a processor for detecting an aircraft based on sensor data collected from the microphones of at least one of the plurality of acoustic sensor installations and a network link for transmitting a signal based on the detection of the aircraft to a control system of the UAV.

A method is provided. An unmanned aerial vehicle (UAV) is operated. A position of the UAV is determined while in flight, and a nonce is generated. A Merkel root is generated based at least in part on a timestamp and the position of the UAV. A current block is calculated based at least in part on a previous block, the Merkel root, and the nonce, and the current block, the timestamp, the nonce, the prior block, and the position of the UAV are transmitted.

An aircraft noise monitoring system uses a set of geographically distributed noise sensors to receive data corresponding to events captured by the noise sensors. Each event corresponds to noise that exceeds a threshold level. For each event, the system will receive a classification of the event as an aircraft noise event or a non-aircraft noise event. It will then use the data corresponding to the events and the received classifications to train a convolutional neural network (CNN) in a classification process. After training, when the system receives a new noise event, it will use the CNN to classify the new noise event as an aircraft noise event or a non-aircraft noise event, and it will generate an output indicating whether the new noise event is an aircraft noise event or a non-aircraft noise event.

A method, medium, and system to receive flight parameter data relating to a plurality of flights, the flight parameter data including indications of aircraft performance based navigation (PBN) capabilities, flight plan information, an aircraft configuration, and an airport configuration for the plurality of flights; assign probabilistic properties to the flight parameter data; receive accurate and current position and predicted flight plan information for a plurality of aircraft corresponding to the flight parameter data; determine a probabilistic trajectory for two of the plurality of aircraft based on a combination of the probabilistic properties of the flight parameter data and the position and predicted flight plan information, the probabilistic trajectory being specific to the two aircraft and including a target spacing specification to maintain a predetermined spacing between the two aircraft at a target location with a specified probability; and generate a record of the probabilistic trajectory for the two aircraft.

A communication optimization system/method for mobile networks uses a server that generates waypoints based on a first communication network within a route to be travelled by an aerial vehicle, the aerial vehicle comprising a communication hub configured to communicate with at least one communication node, a communication hub controller configured control movement of a steerable antenna, and an aerial vehicle controller configured control movement of the aerial vehicle. The server then transmits the waypoints to the aerial vehicle controller; periodically monitors networks not connected to the communication hub; when a second communication network not connected to the communication hub satisfies a threshold, transmits causes the communication controller to steer the steerable antenna in a direction of the second communication network, further causing the communication hub to communicate and connect with the second communication network.

Embodiments include devices and methods for navigating an unmanned autonomous vehicle (UAV) based on a measured magnetic field vector and strength of a magnetic field emanated from a charging station. A processor of the UAV may navigate to the charging station using the magnetic field vector and strength. The processor may determine whether the UAV is substantially aligned with the charging station, and the processor may maneuver the UAV to approach the charging station using the magnetic field vector and strength in response to determining that the UAV is substantially aligned with the charging station. Maneuvering the UAV to approach the charging station using the magnetic field vector and strength may involve descending to a center of the charging station. The UAV may follow a specified route to and/or away from the charging station using the magnetic field vector and strength.

System and methods for real-time virtual visual in-route vehicle monitoring. System and methods herein provide novel means of monitoring global positioning equipped vehicles, in that tamper-proof identifiers registered on vehicle topsides are electronically imaged by aerospace imaging devices, including manned or unmanned aerospace vehicles or satellites. Transponders of in-route vehicles and aerospace imaging devices correspond, said digital images digitally relay to parabolic antennas, relay to connected networks such as the World Wide Web, are transmitted therein and interfaced in real-time via end-users utilizing computerized devices and applications for purposes of real-time virtual visual in-route vehicle monitoring. Crowdsourced end-users are supplied directives and means via a computerized application alert icon to engage said alert icon in order to alert authorities upon occurrences of specified events such as, vehicle well-being concerns, security or terrorism events.

A control device includes: a control signal transmitter transmitting a signal to a controlled device; a control signal receiver receiving a signal from the controlled device; a control signal generator generating a control signal of 2 bytes or more in one transmission cycle, in which an address is assigned to each byte, is ON/OFF switchable, and includes a main signal address and a collation signal address; and a control signal controller that, when the main and collation signal addresses are the same address, turns the collation signal address ON when the main signal address is ON and turns the collation signal address OFF when the main signal address is OFF, and when the main and collation signal addresses are inverted, turns the collation signal address OFF when the main signal address is ON and turns the collation signal address ON when the main signal address is OFF.