Abstract: This disclosure is directed to network optimization in a complex joint network for increasing the network utility of the complex joint network. A computing device in the complex joint network may receive a data flow via a complex joint network. The computing device may determine, based on a network template, a mission utility associated with the data flow and a traffic class associated with the data flow. The computing device may control one or more quality of service decisions based at least in part on the mission utility associated with the data flow and the traffic class associated with the data flow.

Abstract: A method for minimizing aircraft collisions, includes detecting a flight of an unmanned aerial system (UAS) in a restricted area and determining a location of a radio frequency (RF) emitter in communication with the UAS. The method includes, at each of a plurality of RF sensors of a network of wireless RF sensors, receiving RF emissions within an RF band pertaining to UAS control, processing the received RF emissions, and transmitting data derived from the processed RF emissions. The method further includes at a designated one of the plurality of RF sensors, receiving the transmitted data from the RF sensors, a processor computing, using the transmitted data received from the RF sensors, a location estimate for the RF emitter and to predict the UAS is flying, and based on the prediction, the processor generating an alert.

Abstract: This disclosure is directed to network optimization in a complex joint network for increasing the network utility of the complex joint network. A computing device in the complex joint network may receive a data flow via a complex joint network. The computing device may determine, based on a network template, a mission utility associated with the data flow and a traffic class associated with the data flow. The computing device may control one or more quality of service decisions based at least in part on the mission utility associated with the data flow and the traffic class associated with the data flow.

Abstract: A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.

Abstract: This disclosure is directed to network optimization in a complex joint network for increasing the network utility of the complex joint network. A computing device in the complex joint network may receive a data flow via a complex joint network. The computing device may determine, based on a network template, a mission utility associated with the data flow and a traffic class associated with the data flow. The computing device may control one or more quality of service decisions based at least in part on the mission utility associated with the data flow and the traffic class associated with the data flow.

Abstract: A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from one or more of the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.

Abstract: A method for establishing and controlling a mobile perimeter and for determining a geographic location of an emitting radio frequency (RF) emitter in a vicinity of the mobile perimeter includes at each of a plurality of RF sensors, receiving an RF transmission, processing the received RF transmission to produce RF signal data, and wirelessly transmitting the RF signal data to a central station. At the central station, a processor: executes a cross-correlation process to: compare characteristics of pairs of RF signal data; based on the compared characteristics, determine a time difference of arrival (TDOA) between RF transmissions received at each RF sensor of a pair of RF sensors, and repeats the cross-correlation process for each pair of RF sensors from which RF signal data are received. Using the TDOA values for two or more pairs of RF sensors, the processor determines a location estimate for the RF emitter.