An electronic system and method controls automatic or semi-automatic docking of a vehicle with a given docking area, applicable, in particular, to the docking of an airport vehicle, such as a baggage belt loader, a catering vehicle, etc., to the fuselage of an aircraft, for example to the door of such an aircraft. The given docking area comprises at least one target. The system includes first determination device configured to determine the position of the docking area by determining the type of target from a set of given types and its position, second determination device configured to determine a guide path for guiding the vehicle towards the given docking area depending on the position of the docking area, and third determination device configured to determine the type of docking destination, the second determination device being capable of determining one or more exclusion areas depending on the type of docking destination, by comparing the type of docking destination with types of docking destination, stored in a database in association with exclusion areas, such that the guide path for guiding the vehicle towards the given docking area does not pass into any of the exclusion areas.

Automated air traffic control systems and methods may include one or more sensors, such as radar sensors, that are positioned and oriented at opposite ends of a runway. The sensors may detect aerial vehicles on the runway, as well as aerial vehicles within approach corridors at opposite ends of the runway, and other aerial vehicles proximate the runway. Based on data received by the sensors, various characteristics of aerial vehicles can be determined, and instructions for the aerial vehicles can be determined based on the detected characteristics. Then, the aerial vehicles may utilize the determined instructions to coordinate their operations proximate the runway, which may include takeoff, taxiing, and/or landing operations. Further, speech-to-data processing may be used to translate between data and speech or audio input/output in order to enable coordination between unmanned aerial vehicles, manned aerial vehicles, and combinations thereof.

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.

In some examples, a ground obstacle detection system of an aircraft is configured to generate and display a graphical user interface (GUI) that includes a graphical representation of a detected obstacle with which the aircraft may collide during a ground operation and an indication of an area of unknown associated with the detected obstacle. Instead of, in addition to, a GUI that includes an indication of an area of unknown associated with an obstacle, in some examples, a ground obstacle detection system to generate a GUI that includes at least two windows that present different views of an aircraft. At least one of the windows may include a graphical representation of an obstacle that may not be visible in the view of another window.

The disclosure relates to a method for guiding a pilot of an approaching aircraft to a stop position at a stand, said method being characterized by: monitoring a position of the approaching aircraft within a volume at the stand, comparing said monitored position with a first area, said first area enclosing the stop position, comparing said monitored position with a subsection of the first area enclosing the stop position, if said monitored position is inside said subsection: transmitting information to a display to show an indication to the pilot to proceed approaching the stand, and if said monitored position is inside the first area but not inside said subsection: transmitting information to the display to show an indication to the pilot to stop the aircraft. The disclosure further relates to an aircraft docking system.

A system includes a first smart sensor, a second smart sensor, and at least one image processor. The first smart sensor is configured to sense light in a forward direction and to capture an image during a first time period. The second sensor is configured to sense light in the forward direction and to capture a second image during the first time period. The at least one image processor is configured to identify at least one object in the first and second image, to determine a first size of the at least one object in the first image and a second size of the at least one object in the second image, and to determine a distance of the at least one object from the aircraft based upon the first size and the second size.

The disclosure relates to a method for guiding a pilot of an approaching aircraft to a stop position at a stand, said method being characterized by: monitoring a position of the approaching aircraft within a volume at the stand, comparing said monitored position with a first area, said first area enclosing the stop position, comparing said monitored position with a subsection of the first area enclosing the stop position, if said monitored position is inside said subsection: transmitting information to a display to show an indication to the pilot to proceed approaching the stand, and if said monitored position is inside the first area but not inside said subsection: transmitting information to the display to show an indication to the pilot to stop the aircraft. The disclosure further relates to an aircraft docking system.

A radar detection system according to the present disclosure is a radar detection system that detects an obstacle to an aircraft in an airport. The radar detection system includes a radar apparatus, and a control apparatus. The radar apparatus detects an obstacle by transmitting and receiving a radar signal, and acquires obstacle information regarding the obstacle detected based on the received radar signal. The control apparatus switches the operation of the radar apparatus according to the aircraft state of the aircraft. The control apparatus turns off the radar apparatus when the aircraft takes off, and turns on the radar apparatus when the aircraft makes a landing.

In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.

In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.