One embodiment of the present invention relates to a method for performing a vehicle image reconstruction by a sensing vehicle (SV) in a wireless communication system, the method comprising: receiving a plurality of stepped-frequency-continuous-wave (SFCW) from target vehicle (TV); receiving signature waveforms in a different frequency range for the plurality of SFCWs; performing synchronization by using phase-difference-of-arrival (PDoA) based on the signature waveforms; reconstructing one or more virtual images of the TV; and deriving a real image form the determined one of more Virtual Image.

An information processing device according to one aspect of the present invention includes at least one memory storing instructions; and at least one processor coupled to the memory and configured to execute the instructions to: extract a candidate point which is a point contributing to a signal at a target point, based on a position of the target point in a three-dimensional space and a shape of an observed object, the target point being a point specified in an intensity map of the signal from the observed object, the intensity map being acquired by a radar; and generate an image indicating a position of the candidate point in a spatial image capturing the observed object.

In a method for the recognition of an object by means of a radar sensor system, a primary radar signal is transmitted into an observation space, a secondary radar signal reflected by the object is received, a Micro-Doppler spectrogram of the secondary radar signal is generated, and at least one periodicity quantity relating to an at least essentially periodic motion of a part of the object is determined based on the Micro-Doppler spectrogram. The determining of the at least one periodicity quantity includes the following steps: (i) determining the course of at least one periodic signal component corresponding to an at least essentially periodic pattern of the Micro-Doppler spectrogram, (ii) fitting a smoothed curve to the periodic signal component, (iii) determining the positions of a plurality of peaks and/or valleys of the smoothed curve, and (iv) determining the periodicity quantity based on the determined positions of peaks and/or valleys.

A data generation device is provided with environment setting means (200), model setting means (210), image calculation means (220) and data output means (230). The environment setting means sets a radar parameter that indicates a specification of a radar that is a synthetic aperture radar or an inverse synthetic aperture radar. The model setting means sets a three-dimensional model that indicates a shape of a target object to identify. The image calculation means calculates a simulation image based on the three-dimensional model and the radar parameter. The data output means outputs training data in that the simulation image and a type of the target object are associated to each other. In addition, the data output means outputs difference data that indicate a difference between a radar image and the simulation image. The model setting means changes the three-dimensional model based on model correction data inputted based on the difference data.

A synthetic aperture radar (SAR) system is disclosed. The SAR comprises a memory, a convolutional neural network (CNN), a machine-readable medium on the memory, and a machine-readable medium on the memory. The machine-readable medium storing instructions that, when executed by the CNN, cause the SAR system to perform operations. The operation comprises: receiving range profile data associated with observed views of a scene; concatenating the range profile data with a template range profile data of the scene; and estimating registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.

A method for generating data regarding individuals in an area of interest including operating a radar system which may be deployed in the area of interest, to provide a radar image including raw radar data; and/or using a hardware processor configured to store a trained model for analyzing the radar image, thereby to generate object recognition data, wherein the raw radar data generated by the radar system both undergoes signal processing, thereby to generate processed radar data which is used for said training, and is used directly, without signal processing, for training said model.

A synthetic aperture radar image analysis system 20 includes: a phase correlation determination means 21 which determines a strength of the phase correlation between a plurality of pixels in an image selected from among a plurality of images on the basis of the plurality of images that have been photographed by a synthetic aperture radar and show the same point; a shape determination means 22 which determines a degree of similarity between the shape of the distribution of the plurality of pixels and an object shape indicated by geospatial information; and an association means 23 which associates the plurality of pixels with the object on the basis of the determined strength of the phase correlation and the determined degree of similarity.

Persistent scatterers on images and a target object are readily associated with each other. There is provided an image processing apparatus including a persistent scatterer specifier, a phase acquirer, and a clustering unit. The persistent scatterer specifier of the image processing apparatus specifies persistent scatterers at which reflection is stable in a plurality of images. The phase acquirer of the image processing apparatus acquires phases of the specified persistent scatterers. The clustering unit of the image processing apparatus clusters the persistent scatterers based on the positions of the persistent scatterers and the phases.

Embodiments herein describe a scanning station for identifying an air gap between one or more items stored in a container (e.g., a cardboard box) and a surface of the container (e.g., a top lid of the cardboard box). After identifying the air gap, in one embodiment, the scanning station provides instructions to a downstream cutting station where the container is cut opened. In one embodiment, the scanning station includes one or more articulating arms that each includes a scanner (e.g., a radar sensor) attached on an end of the articulating arm facing the container. Moving the articulating arms along the boundaries of the container provides a 3D image of the inside of the container. By processing this image, the scanning station can identify an air gap along a desired cut line as well as a thickness of the sides of the container.

A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.