An interface includes a human machine interface (HMI) to provide a visualization of current and future states of a mission associated with one or more unmanned vehicles. A time specifier provides input to the HMI to specify a current time or a future time for the visualization of the current and future states of the mission. A prediction engine generates predictions of the current and future states of the mission for the visualization based on the current time or the future time specified by the time specifier.

A thorough understanding of aircraft operations counts at airports is helpful due to the use of those counts in the planning and design process and in the allocation of funds for improvement. Methods of counting aircraft operations at airports lacking full-time personnel are typically based on conventional statistical sampling using relatively small sample sizes due to the inherent difficulty and expense of positioning acoustic or pneumatic counting devices at those airports for extended periods of time. Such methods are often inaccurate because of the lack of sufficient representative samples. A means of counting operations using a combination of Mode C, Mode S, and ADS-B extended squitter aircraft transponder data received using a 1090 MHz software-defined radio system is disclosed herein. The increasing presence of such signals in both controlled and uncontrolled airspace around airports, due to a recent federal mandate that all aircraft in certain types of controlled airspace be equipped with ADS-B Out capability by 2020, lends itself to the measurement of operational parameters associated with the related aircraft. The 1090 MHz signals are received passively; i.e., there is no interrogation from the field-deployed device. The resulting sample counts are typically larger than those determined through conventional data collection procedures. In one aspect, these sample counts are applied to a Bayesian statistical estimation technique, which produces an improved estimate of operations.

A system for displaying information related to the flight of an aerial vehicle that is comprised of a display, a camera that captures real-time video input from its surrounding environment, and a computing device that is coupled to, and communicates with the camera, a display, and one or more aerial vehicles. The computing device maps the current location and orientation of the camera to a display coordinate system. The flight data of the aerial vehicle is also mapped on the same display coordinate system. The computing device displays, on the display, the real-time video output that is comprised of the visual integration of the flight data and the real-time video input in the display coordinate system in relation to the present location and orientation of the camera.

A method, system, and recording medium including a drone and pattern recruiting device configured to recruit a plurality of drones based on a mission, a flocking goal device configured to arrange the plurality of drones in the drone-swarm in a pattern to satisfy the mission, and a changing device configured to adaptively change the pattern of the drone-swarm based on a condition of the mission indicating a needed change and to cause the drone and pattern recruiting device to recruit an additional drone for the needed change.

A remotely deployable network of multi-rotor aircraft and landing stations enable widespread use of multi-rotor aircraft in varied environments and application scenarios. A multi-rotor aircraft having modular components to facilitate a range of applications performs remote operations. Landing stations provide a power source to remote aircraft and facilitate semi-autonomous landing. A computing device facilitates use interaction with a network of multi-rotor aircraft and landing stations that together form a network for transmitting data concerning individual and regional aircraft operations.

A method, system, and recording medium including a mission receiving device configured to receive a mission for the drone-swarm based on a user input, a drone and pattern recruiting device configured to recruit a plurality of drones based on the mission, and a flocking goal device configured to arrange the plurality of drones in the drone-swarm in a pattern to satisfy the mission.

Embodiments of the present invention include devices and methods for monitoring a drone(s). The method includes: receiving information of a drone via a communication unit and displaying the information of the drone on a display panel of a device. The information includes the location of the drone and an icon indicating the location of the drone is displayed on a map rendered on the display panel. The method further includes determining, based on the information, whether the drone poses a danger and, responsive to the danger, issuing a warning of the danger.

In an object, for example, a pilotless flying object detection system, an omnidirectional camera as a first camera images a monitoring area. A microphone array acquires audio of the monitoring area. A monitoring apparatus uses the audio data acquired by the microphone array to detect a pilotless flying object which appears in the monitoring area. A signal processor in the monitoring apparatus superimposes a discrimination mark, obtained by converting the pilotless flying object into visual information, on image data of the monitoring area when displaying the image data of the monitoring area captured by the omnidirectional camera on a monitor.

This disclosure is directed to an automated unmanned aerial vehicle (UAV) self-identification system, devices, and techniques pertaining to the automated identification of individual UAVs operating within an airspace via a mesh communication network, individual UAVs and a central authority representing nodes of the mesh network. The system may detect nearby UAVs present within a UAV's airspace. Nearby UAVs may self-identify or be identified via correlation with one or more features detected by the UAV. The UAV may validate identifying information using a dynamic validation policy. Data collected by the UAV may be stored in a local mesh database and distributed to individual nodes of the mesh network and merged into a common central mesh database for distribution to individual nodes of the mesh network. UAVs on the mesh network utilize local and central mesh database information for self-identification and to maintain a dynamic flight plan.

A method, system, and recording medium including a mission receiving device configured to receive a mission for the drone-swarm based on a user input, a drone and pattern recruiting device configured to recruit a plurality of drones based on the mission, and a flocking goal device configured to arrange the plurality of drones in the drone-swarm in a pattern to satisfy the mission.