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.

A method for detecting objects and labeling the objects with distances in an image includes steps of: obtaining a thermal image from a thermal camera, an RGB image from an RGB camera, and radar information from an mmWave radar; adjusting the thermal image based on the RGB image to generate an adjusted thermal image, and generating a fused image based on the RGB image and the adjusted thermal image; generating a second fused image based on the fused image and the radar information; detecting objects in the images, and generating, based on the fused image, another fused image including bounding boxes marking the objects; and determining motion parameters of the objects.

Disclosed is a synthetic aperture radar (SAR) system for target recognition with complex range profile. The SAR system comprising a memory, a recurrent neural network (RNN), a multi-layer linear network in signal communication the RNN, and a machine-readable medium on the memory. The machine-readable medium is configured to store instructions that, when executed by the RNN, cause the SAR system to perform various operations. The various operation comprise: receiving raw SAR data associated with observed views of a scene, wherein the raw SAR data comprises information captured via the SAR system; radio frequency (RF) preprocessing the received raw SAR data to produce a processed raw SAR data; converting the processed raw SAR data to a complex SAR range profile data; processing the complex SAR range profile data with the RNN having RNN states; and mapping the RNN states to a target class with the multi-layer linear network.

The ship candidate pixel derivation means 81 regards each individual pixel of an input synthetic aperture radar image as a pixel of interest, and determines, for each pixel of interest, whether or not the pixel of interest is a ship candidate pixel which is a candidate of a pixel representing a ship, for each combination of a cell size of a target cell including the pixel of interest and a cell size of a guard cell that corresponds to an area between a background cell including pixels around the target cell and the target cell. The integration means 82 integrates a plurality of determination results of whether the pixel of interest is the ship candidate pixel or not, for each pixel of interest. The ship pixel detection means 83 detects the pixel corresponding to the ship, based on integration results obtained for each pixel of interest by the integration means 82.

Apparatus and method configured to determine locations of man-made objects within synthetic aperture radar (SAR) imagery. The apparatus and method prescreen SAR imagery to identify potential locations of man-made objects within SAR imagery. The potential locations are processed using a change detector to remove locations of natural objects to produce a target image containing location of substantially only man-made objects.

The invention relates to a measuring system (2) for measuring a measured object, in particular a plastic profile (3), said measuring system (2) comprising: an antenna arrangement (4) made of a plurality of THz transceivers (5) each at times actively emitting a THz transmission beam (6) and at times passively receive reflected THz radiation (11), where said antenna arrangement (4) puts out measuring signals (S1) of the measurements of the THz transceivers (5), an adjustment means (12) for adjusting the antenna matrix (4) into several measuring positions (MP1, MP2, MP3) along an adjustment direction (m1, m2), e.g. on a circular path around the measured object (3), a controller and evaluation device (14) for receiving and evaluating the measuring signals (S1) which is configured in such a way that the measuring signals (S1) of said several THz transceivers (5) in said several measuring positions (MP1, MP2, . . . ) are evaluated by means of an SAR evaluation process and a virtual model (VM) of the boundary surfaces (8) of the measured object (3) is created, and subsequently the controller and evaluation device (14) determines layer thicknesses (d, d4) between the boundary surfaces (8) from the virtual model (VM).

A millimeter wave image based human body foreign object detection method, comprising: acquiring a millimeter wave gray scale image of a human body; according to a pre-determined foreign object imaging characteristic, extracting from the millimeter wave gray scale image a foreign object area image; according to a pre-determined foreign object image recognition algorithm, performing calculations on the foreign object area image, and acquiring a foreign object image from the foreign object area image; displaying the foreign object image as a foreign object detection result. Also provided is a millimeter wave image based human body foreign object detection system.

A vehicle and a system and method of operating the vehicle. The system includes an extended radar array, a processor and a controller. The extended radar array is formed by moving a radar array of the vehicle through a selected distance. The processor is configured to receive a plurality of observations of an object from the extended radar array, operate a neural network to generate a network output signal based on the plurality of observations, and determine an object parameter of the object with respect to the vehicle from the network output signal. The controller operates the vehicle based on the object parameter of the object.

Discussed herein are systems, devices, and methods for automatic target recognition based on a non-visible input image. A method can include providing, as input to a first machine learning (ML) model for object classification, pixel data of a non-visible image, the first ML model including an encoder from a second ML model, the second ML model trained to generate a visible image representation of an input non-visible image, and receiving, from the first ML model, data indicating one or more objects present in the non-visible image.

A microwave and millimeter wave imaging system. In either a far-field or a near-field detection mode, a radially-polarized probe transmits an imaging signal along a predetermined scan path to detect a target in a sample. The imaging signal's orientation is independent of the target's orientation and changes at each target as the probe transmits the signal during scanning. A measurement system receives scattered waves reflected from the sample via a single channel and images the sample and the target based on the reflected waves independent of the orientation of the target.