Rockwell Collins, Inc.

John W Borghese

Arlen E Breiholz

Roger A Dana

Sethu R Rathinam

Ryan M Murphy

Jean-Phillippe Wasselin

Ella M Atkins

A computing device may include operational processors, configured to execute a set of program instructions, wherein the set of program instructions is configured to cause the operational processors to: receive input signals indicative of input conditions; determine input conditions based input signals; determine output signals based on the determined input conditions; and provide determined output signals. The computing device may further include machine-learning processors, wherein the machine-learning processors are configured to develop machine-learning analyzers, wherein the machine-learning analyzers are configured to: identify operational parameters of the operational processors; determine modifications to the set of program instructions, wherein the modifications satisfy a selected quality metric; and provide the modifications to the operational processors.

Machine-learned operating system and processor

A system and method for detection and identification of an Unmanned Aircraft Systems (UAS) employs a radar system to detect and identify the UAS based on the rich Doppler spectrum generated by one or more rotors and associated motors onboard the UAS. UAS have a low radar cross sections (RCS), relatively low speed, and possess a unique Doppler signature providing data for the system to discriminate once the system detects the quadcopter UAS. The system and method functions as a traditional radar, yet analyzes the micro-Doppler signature, including the RCS and radial speed, to detect and identify the UAS. Based on the signature analysis, the system and method are able to distinguish one model from other types of UAS.

Radar detection and discrimination of quadcopters using measured Doppler signatures system and method

An apparatus and method can provide a warning of a drone or unmanned aerial vehicle in the vicinity of an airport. The apparatus can include at least one antenna directionally disposed at an along the approach or departure path and a detector configured to provide a warning of a presence of sense an unmanned aerial or drone. The warning can be provided in response to a radio frequency signal received by the at least one of the antenna being in a frequency band associated with a transmission frequency for the unmanned aerial vehicle or drone or in a frequency band associated with interaction from receive circuitry of the unmanned aerial vehicle or drone.

System for and method of detecting drones

A present novel and non-trivial system and method for generating trajectory data for an aircraft in flight is disclosed. Data representative of an aircraft's current trajectory, aircraft parameter(s), and navigation service data are received. As embodied herein, the aircraft parameter(s) may be received from more than one aircraft. An optimized flight trajectory for the aircraft is determined from a trajectory optimizing function and based upon the aircraft's current trajectory data, the aircraft parameter(s) data, and the navigation service data. Then, dispatch center trajectory data representative of the optimized flight trajectory is generated and sent to at least one user such as an aircraft system of the aircraft and/or an air navigation service provider. As embodied herein, the trajectory optimization function could be based upon cost minimization, time minimization, an operator's internal prioritization, or any combination of these.

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Cedar Rapids, IA, United States of America

John W Borghese