A monitoring system for an aircraft uses sensors configured to sense objects around the aircraft to generate a recommendation that is ultimately used to determine a possible route that the aircraft can follow to avoid colliding with a sensed object. A first algorithm generates guidance to avoid encounters with sensed airborne aircrafts. A second algorithm generates guidance to avoid encounters with sensed non-aircraft airborne obstacles and ground obstacles. The second algorithm sends inhibiting information to the first algorithm in a feedback loop based on the position of sensed non-aircraft objects. The first algorithm considers this inhibiting information when generating avoidance guidance regarding airborne aircrafts.

A monitoring system for an aircraft can include an image sensor and a radar sensor. The system can provide noise compensation to a radar sample corresponding to a return radar signal received by the radar sensor based on information detected by the image sensor. The system can identify one or more object types in the image captured by the image sensor and then translate the identified object types to corresponding positions on a map. The system can correlate the radar sample to a position on the map and any object type located at that position can be identified. The system can then select a noise pattern that corresponds to the identified object type from the map and use the selected noise pattern to compensate the radar sample.

A monitoring system for an aircraft can include an image sensor and a radar sensor. The system can provide noise compensation to a radar sample corresponding to a return radar signal received by the radar sensor based on information detected by the image sensor. The system can identify one or more object types in the image captured by the image sensor and then translate the identified object types to corresponding positions on a map. The system can correlate the radar sample to a position on the map and any object type located at that position can be identified. The system can then select a noise pattern that corresponds to the identified object type from the map and use the selected noise pattern to compensate the radar sample.

An example autonomous or remotely piloted aircraft includes a virtual aperture radar system including a plurality of antennas relationally positioned on one or more surfaces of the aircraft such that individual beams from each of the plurality of antennas scan respective volumes around the aircraft and the respective volumes together substantially form an ellipsoidal field of regard around the aircraft, and a computing device having one or more processors configured to execute instructions stored in memory for performing functions of: combining the respective volumes together to form an image representative of the ellipsoidal field of regard around the aircraft, and identifying one or more objects within the image.

A radar-absorbing structure is disclosed which includes a fiber, at least one binding agent disposed on the fiber and thus enabling the fiber to bind to another fiber.

A radar-absorbing structure is disclosed which includes a fiber, at least one binding agent disposed on the fiber and thus enabling the fiber to bind to another fiber.

The present disclosure relates to a cargo protection method, device and system, and a non-transitory computer-readable storage medium, relating to the technical field of unmanned aerial vehicles. The method of the present disclosure includes: determining whether an unmanned aerial vehicle is in a falling state or not according to a current acceleration in a vertical direction of the unmanned aerial vehicle and a current vertical distance from the unmanned aerial vehicle to the ground; and opening at least one airbag in a cargo hold of the unmanned aerial vehicle in a case where the unmanned aerial vehicle is in the falling state to protect a cargo in the cargo hold.

The present disclosure relates to a cargo protection method, device and system, and a non-transitory computer-readable storage medium, relating to the technical field of unmanned aerial vehicles. The method of the present disclosure includes: determining whether an unmanned aerial vehicle is in a falling state or not according to a current acceleration in a vertical direction of the unmanned aerial vehicle and a current vertical distance from the unmanned aerial vehicle to the ground; and opening at least one airbag in a cargo hold of the unmanned aerial vehicle in a case where the unmanned aerial vehicle is in the falling state to protect a cargo in the cargo hold.

A method of collision warning using broad antenna pattern ultra-wide band (UWB) radar includes emitting a first radar ping from a broad beam UWB antenna and receiving a first return signal identifying an object. A first hemisphere with a first radius is determined for the object. A second ping, second return and second hemisphere is defined for the object. At the intersection of the hemispheres, an object ring is defined. The radius of the object ring is compared with the radius of a collision cylinder (e.g., representing a safe distance around a system or device, such as a drone). The object may be identified as posing a collision threat when the radius of the object ring is smaller than the radius of the collision cylinder.

A method of collision warning using broad antenna pattern ultra-wide band (UWB) radar includes emitting a first radar ping from a broad beam UWB antenna and receiving a first return signal identifying an object. A first hemisphere with a first radius is determined for the object. A second ping, second return and second hemisphere is defined for the object. At the intersection of the hemispheres, an object ring is defined. The radius of the object ring is compared with the radius of a collision cylinder (e.g., representing a safe distance around a system or device, such as a drone). The object may be identified as posing a collision threat when the radius of the object ring is smaller than the radius of the collision cylinder.