An active three-dimensional scene information acquisition method based on dimensionality-reduced sparse representation is provided. The method jointly processes multiple one-dimensional active detection signals collected synchronously to achieve three-dimensional positioning of objects in a detected scene or three-dimensional reconstruction of a scene structure. Through an active detection system equipped with one transmitter and multiple receivers, simultaneous three-dimensional positioning of multiple targets in a scene or three-dimensional reconstruction of the geometry of the scene is achieved.

A method for use in acoustic imaging, comprising: transmitting, from a transmitter, a first sound wave pulse at a first frequency determined by a maximum sampling rate of a receiver; transmitting at least one second sound wave pulse at a frequency substantially equal to the first frequency, the first and at least one second sound wave pulses being transmitted substantially within a fraction of a sample interval of the receiver; receiving and sampling, at the receiver, a reflection of at least two of the first and at least one second pulses to generate a set of receiver samples; and expanding the set of receiver samples, based on the first frequency and a total number of the first and at least one second pulses transmitted, to generate an expanded sample set with a larger number of samples than the set of receiver samples.

An analysis of radar signals, in particular of radar signals, which are received by a plurality of ULA antennas. By applying different beam formations to the radar signals of the individual ULA antennas, beamforming is used to compensate for dips in the gain.

One of a transmitting array antenna and a receiving array antenna includes a first antenna group and a second antenna group. The first antenna group includes one or more first antenna elements of which the phase centers of the antenna elements are laid out at each first layout spacing following a first axis direction, and a shared antenna element. The second antenna group includes a plurality of second antenna elements and the one shared antenna element, and the phase centers of the antenna elements are laid out in two columns at each second layout spacing following a second axis direction that is different from the first axis direction. The phase centers of the antenna elements included in each of the two columns differ from each other regarding position in the second axis direction.

Techniques are disclosed for determining the location of an object using RF sensing. More specifically, an object may be detected in a wireless data communication network using radar techniques in which one or more base stations act as a transmitter and a mobile device (e.g., a user equipment (UE)) acts as a receiver in a bistatic or multi-static radar configuration. By comparing the time a line-of-sight (LOS) signal is received by the mobile device with that of an echo signal from a reflection of an RF signal from the object, a position of the object can be determined. Depending on desired functionality, this position can be determined by the UE, or by a network entity.

The invention provides a method of operating a radio communications system first communication device, the method comprising identifying a position of an object which may comprise at least one second communication device by processing electromagnetic radiation emanating passively from the object; steering at least one of a receive beam and a transmit beam towards the object; and determining whether the object comprises at least one second communication device attempting to establish a radio communication with the first communication device.

An estimation method is provided which includes: calculating a plurality of complex transfer functions, based on reception signals respectively received by N reception antenna elements during a predetermined period, the complex transfer functions each representing propagation characteristics between a transmission antenna element and the N reception antenna elements; extracting a variation component corresponding to each of the N reception antenna elements, from the calculated complex transfer functions, the variation component being caused by a living body; calculating a correlation matrix based on the variation component corresponding to each of the N reception antenna elements; calculating eigenvalues of the correlation matrix calculated in the calculating of the correlation matrix; and estimating the number of living bodies in a predetermined method, using the eigenvalues calculated in the calculating of the eigenvalues.

A method for automatically training a neural network includes at a trainer having a first communication device and a perception recorder, continuously recording the surroundings in the vicinity of the first object; receiving, at the trainer, a message from a communication device associated with an object in the vicinity of the trainer, the message including information about the position and the type of the object; identifying a recording corresponding to the time at which the message is received from the object; correlating the received positional information about the second object with a corresponding location in the recording to identify the object in the recording; classifying the identified object based on the type of information received in the message from the object; and using the classified recording to train the neural network.

A method for automatically training a neural network includes at a trainer having a first communication device and a perception recorder, continuously recording the surroundings in the vicinity of the first object; receiving, at the trainer, a message from a communication device associated with an object in the vicinity of the trainer, the message including information about the position and the type of the object; identifying a recording corresponding to the time at which the message is received from the object; correlating the received positional information about the second object with a corresponding location in the recording to identify the object in the recording; classifying the identified object based on the type of information received in the message from the object; and using the classified recording to train the neural network.

An aircraft includes an airframe with a thrust array attached thereto. The thrust array includes a plurality of propulsion assemblies that are independently controlled by a flight control system. A landing gear assembly is coupled to the airframe and includes a plurality of landing feet. An altitude sensor array includes a plurality of altitude sensors each of which is disposed within one of the landing feet such that when the aircraft is in the VTOL orientation, the altitude sensor array is configured to obtain multifocal altitude data relative to a landing surface. The flight control system is configured to generate a three-dimensional terrain map of the surface based upon the multifocal altitude data.