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 system and method for adaptive extension of a command and control (C2) backhaul network for unmanned aircraft systems (UAS) is disclosed. In embodiments, the adaptive extension includes front-end and back-end cognitive engines on either side of an extension of the C2 backhaul network to a remote control station otherwise without a point of presence (PoP). Both cognitive engines transmit test packets to the other to refine coding schemes for transmission of C2 packets across the extension based on received feedback about the transmission. When C2 packets are received from the backhaul network or remote control station, the cognitive engines select an optimal coding scheme based on packet size. The cognitive engine encodes the C2 packets based on the optimal coding scheme. When C2 packets are received from the other cognitive engine, the applicable coding schemes are identified and the C2 packets decoded accordingly.

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 centralized spectrum arbitrator for a command and control (C2) link system is disclosed. In embodiments, the spectrum arbitrator receives sensor fusion data from the air radio systems (ARS) and ground radio systems (GRS) of the C2 link system, each dataset comprising mean energy levels for a particular frequency and location. The spectrum arbitrator determines a time average of the energy levels and evaluates interference with the frequency at the location (e.g., whether the interference is tolerable or the frequency should not be used) and attempts to classify interfering signals (e.g., as radar, malicious, of the C2 link system or competing). The spectrum arbitrator may further fuse sensor fused data into additional spectrum situational awareness (SA) outputs illustrating or recommending opportunistic frequency use or reuse across the C2 link system.

Abstract: A command and control (C2) radio system configured for same-channel out-of-band sensing is disclosed. In embodiments, the radio system (e.g., an air radio system (ARS) aboard an unmanned aircraft system (UAS) or a ground radio station (GRS)) scans its switching back to the appropriate operating frequency before the next subframe starts. The radio system processes the collected energy samples to determine minimum and mean operating frequencies for idle subframes and slots where a preamble is not detected. The radio system uses idle frames to scan sensing frequencies assigned by a central server of the C2 link system, collecting spectral energy sources during the idle timeslots and energy levels, thereby identifying the level of interference on the assigned frequency (e.g., due to noise or interfering signals) and hypothesizes whether the detected interference is tolerable or precludes current use of the assigned signal in the vicinity of the radio system.

Abstract: A fusion engine configured for execution on a ground radio station (GRS) for an unmanned aircraft system (UAS) operating environment is disclosed. In embodiments, the fusion engine tracks the wait times of UAS required to share a C2 channel while waiting for a full C2 link with the GRS, as well as the dynamic availability of C2 channels for full or shared C2 links over time. Based on the collected wait times and availability variables, the fusion engine generates congestion metrics relevant to the capacity of the GRS, e.g., its available spectrum resources, to effectively provide C2 services to every UAS operating within its coverage area. Congestion metrics include expected wait times on a shared C2 link fora UAS in service (e.g., waiting fora full C2 link) and/or the expected number of C2 channels available at the GRS at a given time.

Abstract: A system and method for adaptive extension of a command and control (C2) backhaul network for unmanned aircraft systems (UAS) is disclosed. In embodiments, the adaptive extension includes front-end and back-end cognitive engines on either side of an extension of the C2 backhaul network to a remote control station otherwise without a point of presence (PoP). Both cognitive engines transmit test packets to the other to refine coding schemes for transmission of C2 packets across the extension based on received feedback about the transmission. When C2 packets are received from the backhaul network or remote control station, the cognitive engines select an optimal coding scheme based on packet size. The cognitive engine encodes the C2 packets based on the optimal coding scheme. When C2 packets are received from the other cognitive engine, the applicable coding schemes are identified and the C2 packets decoded accordingly.

Abstract: A fusion engine configured for execution on a ground radio station (GRS) for an unmanned aircraft system (UAS) operating environment is disclosed. In embodiments, the fusion engine tracks the wait times of UAS required to share a C2 channel while waiting for a full C2 link with the GRS, as well as the dynamic availability of C2 channels for full or shared C2 links over time. Based on the collected wait times and availability variables, the fusion engine generates congestion metrics relevant to the capacity of the GRS, e.g., its available spectrum resources, to effectively provide C2 services to every UAS operating within its coverage area. Congestion metrics include expected wait times on a shared C2 link for a UAS in service (e.g., waiting for a full C2 link) and/or the expected number of C2 channels available at the GRS at a given time.

Abstract: A command and control (C2) radio system configured for same-channel out-of-band sensing is disclosed. In embodiments, the radio system (e.g., an air radio system (ARS) aboard an unmanned aircraft system (UAS) or a ground radio station (GRS)) scans its switching back to the appropriate operating frequency before the next subframe starts. The radio system processes the collected energy samples to determine minimum and mean operating frequencies for idle subframes and slots where a preamble is not detected. The radio system uses idle frames to scan sensing frequencies assigned by a central server of the C2 link system, collecting spectral energy sources during the idle timeslots and energy levels, thereby identifying the level of interference on the assigned frequency (e.g., due to noise or interfering signals) and hypothesizes whether the detected interference is tolerable or precludes current use of the assigned signal in the vicinity of the radio system.

Abstract: A centralized spectrum arbitrator for a command and control (C2) link system is disclosed. In embodiments, the spectrum arbitrator receives sensor fusion data from the air radio systems (ARS) and ground radio systems (GRS) of the C2 link system, each dataset comprising mean energy levels for a particular frequency and location. The spectrum arbitrator determines a time average of the energy levels and evaluates interference with the frequency at the location (e.g., whether the interference is tolerable or the frequency should not be used) and attempts to classify interfering signals (e.g., as radar, malicious, of the C2 link system or competing). The spectrum arbitrator may further fuse sensor fused data into additional spectrum situational awareness (SA) outputs illustrating or recommending opportunistic frequency use or reuse across the C2 link system.

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 aircraft system (UAS) control apparatus is disclosed. In embodiments, the UAS control apparatus is embodied in a control station to manage command and control (C2) functions for UAS operations in a designated coverage volume including a geofenced interference region proximate to the control station, controlling each UAS via connections on a spectrum of C2 channels. The UAS control apparatus generates flight plans for UAS operations, providing separation and keeping UAS operations away from the control station to minimize RF interference with other UAS C2 connections. Should a UAS be required to operate proximate to the control station, the UAS control apparatus employs dynamic spectrum management with respect to other concurrently operating UAS to eliminate, reduce, or mitigate RF interference resulting from the encroaching UAS.

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.