Abstract: A system and method for optimizing mission fulfillment via unmanned aircraft systems (UAS) within a mission space generates or receives atmospheric models forecasting weather and wind through the mission space, the atmospheric models having an uncertainty factor. Until the projected flight time, the controller may iterate through one or more simulations of a projected flight plan through the mission space, determining the probability of successful fulfillment of mission objectives based on the most current atmospheric models (including the ability of the UAS to navigate the flight plan within authorized airspace constraints). Based on conditions and behaviors observed during a simulated flight plan, the controller may revise flight plans, flight times, or atmospheric models for subsequent simulations. Based on multiple probabilities of fulfillment across multiple simulations, the controller selects an optimal flight plan and/or flight time for fulfillment of the assigned set of mission objectives.

Abstract: An unmanned aerial system (UAS) is disclosed. In embodiments, the UAS includes an unmanned aerial vehicle (UAV), and a controller communicatively coupled to the UAV. In embodiments, the UAS controller may be configured to: acquire a command and control (C2) link quality model for a planned route; generate one or more control signals configured to cause the UAV to perform a monitored flight along a planned route; acquire actual C2 link quality data during the monitored flight along the planned route; compare the actual C2 link quality data to the C2 link quality model; identify a C2 link quality deviation between the actual C2 link quality and the C2 link quality model; and generate one or more control signals configured to cause the UAV to perform one or more prescribed flight plan maneuvers if the identified C2 link quality deviation (?C2LQ) exceeds a threshold deviation value (C2thresh).

Abstract: An unmanned aerial system (UAS) is disclosed. In embodiments, the UAS includes an unmanned aerial vehicle (UAV), and a controller communicatively coupled to the UAV. In embodiments, the UAS controller may be configured to: acquire a command and control (C2) link quality model for a planned route; generate one or more control signals configured to cause the UAV to perform a monitored flight along a planned route; acquire actual C2 link quality data during the monitored flight along the planned route; compare the actual C2 link quality data to the C2 link quality model; identify a C2 link quality deviation between the actual C2 link quality and the C2 link quality model; and generate one or more control signals configured to cause the UAV to perform one or more prescribed flight plan maneuvers if the identified C2 link quality deviation (?C2LQ) exceeds a threshold deviation value (C2thresh).

Abstract: An unmanned aerial system (UAS) is disclosed. In embodiments, the UAS includes an unmanned aerial vehicle (UAV) and a controller communicatively coupled to the UAV. In embodiments, the UAS controller may be configured to: acquire a surveillance quality model for a prescribed flight path; generate one or more control signals configured to cause the UAV to perform a monitored flight along the prescribed flight path; acquire actual surveillance quality data during the monitored flight along the prescribed flight path; compare the actual surveillance quality data to the surveillance quality model; identify a surveillance quality deviation between the actual surveillance quality and the surveillance quality model; and generate one or more control signals configured to cause the UAV to perform one or more corrective actions or maneuvers if the identified surveillance quality deviation exceeds a threshold deviation value.

Abstract: A system and method for optimizing mission fulfillment via unmanned aircraft systems (UAS) within a mission space generates or receives atmospheric models forecasting weather and wind through the mission space, the atmospheric models having an uncertainty factor. Until the projected flight time, the controller may iterate through one or more simulations of a projected flight plan through the mission space, determining the probability of successful fulfillment of mission objectives based on the most current atmospheric models (including the ability of the UAS to navigate the flight plan within authorized airspace constraints). Based on conditions and behaviors observed during a simulated flight plan, the controller may revise flight plans, flight times, or atmospheric models for subsequent simulations. Based on multiple probabilities of fulfillment across multiple simulations, the controller selects an optimal flight plan and/or flight time for fulfillment of the assigned set of mission objectives.

Abstract: A method using a radar system includes providing a radar pulse using an antenna, receiving radar returns using the antenna, and detecting target data using the radar returns. The target data is processed using ground data associated with transportation routes. Correlation of a transportation route of the transportation routes and a location of a first target associated with the target data is an indication of ground clutter.

Abstract: A communication link analysis system is for analyzing communication link performance for an unmanned aviation system traveling over a route using a radio network comprising a plurality of radio nodes. The communication link analysis system includes a memory for storing quality parameters associated with communication links between the unmanned aviation system and the radio nodes. The communication link analysis system also includes a processor configured to map the quality parameters by location along the route or provide a bin chart of the quality parameter by radio node. The quality parameter is derived from radio link parameters provided by the unmanned aviation system.

Abstract: Safe practical autonomy is ensured by encapsulating an unreliable or untrusted machine learning algorithm within a control-based algorithm. A safety envelope is utilized to ensure that the machine learning algorithm does not output control signals that are beyond safe thresholds or limits. Secure practical autonomy is ensured by verification using digital certificates or cryptographic signatures. The verification may be for individual partitions of an autonomous system or apparatus. The partitions include trusted and untrusted partitions. Trusted partitions are verified for security, while untrusted partitions are verified for safety and security.

Abstract: Systems for mobile launch and retrieval of unmanned aircraft systems (UAS) include a rail-based, ground-based, or water-based mobile platform carrying a mobile station for launching and retrieving vertical take-off and landing (VTOL) or non-VTOL UAS while the platform is in motion, based on current position and weather conditions. The mobile platform may include facilities for communicating with the airborne UAS, stowing a retrieved UAS, and reloading/refitting a stowed UAS. The mobile platform may include positionable wake control devices and a partially positionable launch and retrieval mechanism for alleviating turbulence or crosswinds. The mobile platform may include long-range sensors for detecting or identifying obstacles near a rail-based platform that may interfere with the operating envelope of the launch and retrieval mechanisms. The launch and retrieval system may be intermodal and scalable either up or down as mission parameters demand.

Abstract: A training data management method and related system is disclosed comprising three integrated elements. A training data network may provide a network linking a remote instructor operating an off-board data network with a student operating an onboard data network. The remote instructor may, via the datalink connection, maintain a level of control of the data set available to the student. A method for providing training data may include an onboard and off-board safety monitor configured for perceiving a safety event and sending a notification thereof to an involved participant. A system for providing training data may include a data guard configured to monitor all communications between two networks and block sensitive information, training data, and classified communications between the networks.

Abstract: A system and method are disclosed for offering a trainee presentation blending live and virtual entities to create a training scenario unconstrained by live entity operational performance and geographical limitations. The system blends an instance of a virtual entity with an actual presentation of a live entity within a trainee presentation. Outside of trainee local sensor range, the system presents a virtual entity to the trainee while occluding local sensor presentation of the live entity. As the scenario progresses to a lessor range or higher criticality, the system offers the live entity emulation information concerning characteristics of the virtual entity so the live entity may anticipate and begin to emulate the virtual. At a crossover point, the system determines if the live entity has successfully emulated the virtual and if so, discontinues presentation of the virtual while removing the occlusion allowing presentation of the live entity.

Abstract: A method for operating a distributed flight management system. The method includes operating a control station instance of the distributed flight management system. The method includes receiving flight management system data from a remotely accessed vehicle. The method includes receiving time-space-position information of the remotely accessed vehicle from the remotely accessed vehicle. The method includes updating the control station instance of the distributed flight management system based at least on the received flight management system data and the time-space-position information of the remotely accessed vehicle. The method includes outputting updated flight management system data for transmission to the remotely accessed vehicle to synchronize a remotely accessed vehicle instance of the distributed flight management system with the control station instance of the distributed flight management system.

Abstract: A system, device, and method for generating and employing risk-based flight path data are disclosed. The system for employing risk-based flight path data may include one or more one avionics systems and/or remote aircraft operator systems configured to receive risk-based flight path data from a route generator (RG). The RG may acquire navigation data representative of one or more waypoints, acquire risk object data based upon the navigation data, determine the risk-based flight path data representative of a risk-based flight path as a function of the acquired navigation data, the acquired risk data, and a route generating algorithm, and provide the flight path data to the one or more avionics systems and/or remote aircraft operator systems. In some embodiments, the risk object data may include a plurality of risk clearance altitudes. In other embodiments, the risk object data may include a plurality of risk clearance elevations.

Abstract: A system and related method is disclosed for applying a training tag to an entity within a training scenario and presenting the entity based on the applied tag. The method receives sensor data from of a plurality of onboard sensors, truth data and state data via a training and tactical network, and simulation data representative of Live, Virtual and Constructive training entities. The method applies Multi Sensor Integration techniques to the received data to display a single presentation where appropriate within the scenario. Based on a defined set of attributes, the method attaches a training specific tag to each integrated representative data set and applies a rules set to determine whether and how to present each received and tagged training entity to a trainee.

Abstract: A system and method is disclosed for correlation of received objects offering a valid presentation to an operator. The method correlates actual objects and simulated objects to offer a valuable training presentation environment to an operator. The method may receive a plurality of data streams including sensed data, simulated sensed data, and truth data to correlate among the plurality of data to determine if the data corresponds to a common object. Each of these data streams may be received from an off-board source via datalink or generated by an onboard simulation data source. The system correlates data received from an onboard source with data received from an off-board source to present the best available training scenario to the operator.

Abstract: A system and method is disclosed for correlation of received objects offering a valid presentation to an operator. The method correlates actual objects and simulated objects to offer a valuable training presentation environment to an operator. The method may receive a plurality of data streams including sensed data, simulated sensed data, and truth data to correlate among the plurality of data to determine if the data corresponds to a common object. Each of these data streams may be received from an off-board source via datalink or generated by an onboard simulation data source. The system correlates data received from an onboard source with data received from an off-board source to present the best available training scenario to the operator.

Abstract: A training data management method and related system is disclosed comprising three integrated elements. A training data network may provide a network linking a remote instructor operating an off-board data network with a student operating an onboard data network. The remote instructor may, via the datalink connection, maintain a level of control of the data set available to the student. A method for providing training data may include an onboard and off-board safety monitor configured for perceiving a safety event and sending a notification thereof to an involved participant. A system for providing training data may include a data guard configured to monitor all communications between two networks and block sensitive information, training data, and classified communications between the networks.