An in-flight safety enhancing system including: a combined automatic dependent surveillance broadcast (ADS-B) and carbon monoxide (CO) detecting device configured to receive an ADS-B transmission and obtain a CO reading; and a flight application executing on an aircraft crew computing device separate from the combined ADS-B and CO detecting device, and configured to: receive the ADS-B transmission and the CO reading; augment the flight application with information extracted from the ADS-B transmission; and provide a CO status notification when the CO reading exceeds a CO threshold value.

The present disclosure relates to a method for determining an action for collision avoidance in a craft. The method (100) comprises obtaining (110) object data comprising three-dimensional object data points (420); obtaining (120) state data of the craft (260); determining (140) at least one set of manoeuvre paths (410a,b,c) for the craft (260) based on the obtained craft state data; determining (150) a set of distance thresholds (421) for the three-dimensional object data points (420) based on the object data; comparing (160) each set of manoeuvre paths (410a,b,c) with the object data and the set of distance thresholds (421), wherein the set of manoeuvre paths (410a,b,c) is identified as a colliding set of manoeuvre paths (410a,b,c) when each path of the set of manoeuvre paths (410a,b,c) is at least partially within the corresponding distance threshold (421) of at least one three-dimensional object data point (420); and determining (170) an action upon identification of at least one colliding set of manoeuvre paths (410a,b,c).

The method can include: determining sensor information with an aircraft sensor suite; based on the sensor information, determining a flight command using a set of models; validating the flight command S130; and facilitating execution of a validated flight command. The method can optionally include generating a trained model. However, the method S100 can additionally or alternatively include any other suitable elements. The method can function to facilitate aircraft control based on autonomously generated flight commands. The method can additionally or alternatively function to achieve human-in-the-loop autonomous aircraft control, and/or can function to generate a trained neural network based on validation of autonomously generated aircraft flight commands.

The method can include: determining sensor information with an aircraft sensor suite; based on the sensor information, determining a flight command using a set of models; validating the flight command S130; and facilitating execution of a validated flight command. The method can optionally include generating a trained model. However, the method S100 can additionally or alternatively include any other suitable elements. The method can function to facilitate aircraft control based on autonomously generated flight commands. The method can additionally or alternatively function to achieve human-in-the-loop autonomous aircraft control, and/or can function to generate a trained neural network based on validation of autonomously generated aircraft flight commands.

An apparatus is provided for detecting and avoiding objects in real-time. The apparatus includes a first sensor that collects environmental data and a second sensor that collects external data corresponding to an external object within a detection range from the apparatus. The apparatus further includes a processor that, in real-time, calculates a minimum distance to avoid the external object, based at least in part on the environmental data and the external data, monitors the environmental data and the external data, and controls the apparatus to avoid the external object based on the calculated minimum distance and the monitored environmental data and external data.

A situational awareness system for aerial refueling is disclosed. In embodiments, the situational awareness system may include one or more millimeter wave devices of a tanker aircraft configured to transmit one or more emitter signals toward an aircraft to be refueled, and receive one or more reflected signals reflected from the aircraft to be refueled. In embodiments, the situational awareness system further includes a controller communicatively coupled to the one or more millimeter wave devices and the display device, the controller configured to determine a distance between a refueling assembly of the tanker aircraft and the aircraft to be refueled based on the one or more reflected signals, and generate one or more control signals configured to cause a display substrate of a display device to display at least one of a metric or image indicative of the determined distance.

A situational awareness system for aerial refueling is disclosed. In embodiments, the situational awareness system may include one or more millimeter wave devices of a tanker aircraft configured to transmit one or more emitter signals toward an aircraft to be refueled, and receive one or more reflected signals reflected from the aircraft to be refueled. In embodiments, the situational awareness system further includes a controller communicatively coupled to the one or more millimeter wave devices and the display device, the controller configured to determine a distance between a refueling assembly of the tanker aircraft and the aircraft to be refueled based on the one or more reflected signals, and generate one or more control signals configured to cause a display substrate of a display device to display at least one of a metric or image indicative of the determined distance.

According to an example aspect of the present invention, there is provided a method comprising, receiving, from a radar, a first reflected signal and a second reflected signal, determining a reference signal of the first reflected signal and training an artificial neural network using the first reflected signal and the reference signal of the first reflected signal, upon training, determining an output of the artificial neural network associated with the first reflected signal and providing a magnitude and angle image of the radar associated with the second reflected signal based on the output of the artificial neural network associated with the first reflected signal.

A collision avoidance system includes a monopulse radar antenna array of monopulse radar antenna segments mounted to a vehicle with respective fixed fields of view. Each monopulse radar antenna segment comprises a comparator network configured to form a sum signal representing a summation of return signals and a first difference signal representing a first difference of the return signals. The system further includes a user interface configured to present information in a form perceptible to a person operating the vehicle and a radar antenna array controller configured to calculate a range of the object and a first (azimuth) angle of arrival of the return signal from the object. The comparator network is further configured to form a second difference signal which the radar antenna array controller uses to calculate a second (elevation) angle of arrival.

A collision avoidance system includes a monopulse radar antenna array of monopulse radar antenna segments mounted to a vehicle with respective fixed fields of view. Each monopulse radar antenna segment comprises a comparator network configured to form a sum signal representing a summation of return signals and a first difference signal representing a first difference of the return signals. The system further includes a user interface configured to present information in a form perceptible to a person operating the vehicle and a radar antenna array controller configured to calculate a range of the object and a first (azimuth) angle of arrival of the return signal from the object. The comparator network is further configured to form a second difference signal which the radar antenna array controller uses to calculate a second (elevation) angle of arrival.