The invention concerns a method (1) to detect deformations of, and/or damages to, structures permanently arranged on the earth's surface. In particular, said method (1) comprises: acquiring (11) georeferencing data indicative of geographical reference positions of predefined points of interest of a given structure to be monitored permanently arranged on the earth's surface, wherein said predefined points of interest are representative of a 3D geometry of the given structure without deformations and damages; acquiring (12) a SAR image of an area of the earth's surface where the given structure is arranged, wherein said SAR image is associated with a given reference coordinate system; transforming (13) the geographical reference positions of the predefined points of interest into corresponding expected positions in the given reference coordinate system associated with the acquired SAR image so as to carry out a reprojection of the 3D geometry of the given structure without deformations and damages on the acquired SAR image; identifying (14) in the acquired SAR image the predefined points of interest of the given structure; determining (15) actual positions in the given reference coordinate system associated with the acquired SAR image of the predefined points of interest identified in said SAR image; making a comparison (16) between the expected positions of the predefined points of interest and the corresponding actual positions in the acquired SAR image; and detecting (17) one or more deformations of, and/or one or more damages to, said given structure on the basis of the comparison made.

The invention concerns a method (1) to detect deformations of, and/or damages to, structures permanently arranged on the earth's surface. In particular, said method (1) comprises: acquiring (11) georeferencing data indicative of geographical reference positions of predefined points of interest of a given structure to be monitored permanently arranged on the earth's surface, wherein said predefined points of interest are representative of a 3D geometry of the given structure without deformations and damages; acquiring (12) a SAR image of an area of the earth's surface where the given structure is arranged, wherein said SAR image is associated with a given reference coordinate system; transforming (13) the geographical reference positions of the predefined points of interest into corresponding expected positions in the given reference coordinate system associated with the acquired SAR image so as to carry out a reprojection of the 3D geometry of the given structure without deformations and damages on the acquired SAR image; identifying (14) in the acquired SAR image the predefined points of interest of the given structure; determining (15) actual positions in the given reference coordinate system associated with the acquired SAR image of the predefined points of interest identified in said SAR image; making a comparison (16) between the expected positions of the predefined points of interest and the corresponding actual positions in the acquired SAR image; and detecting (17) one or more deformations of, and/or one or more damages to, said given structure on the basis of the comparison made.

A close-range microwave imaging method includes: implementing Fourier transform in a pre-set rotation axis direction on an echo signal reflected from a target object and acquired by rotating an array antenna around the pre-set rotation axis to obtain a first echo signal, wherein the first echo signal is represented in polar coordinates; multiplying the first echo signal by a pre-set reference function to obtain a second echo signal; converting the second echo signal into rectangular coordinates by a pre-set algorithm to obtain a third echo signal; and implementing three-dimensional Fourier transform on the third echo signal to obtain three-dimensional image data of the target object. By means of the method, three-dimensional image data of a target object can be obtained fast, fast imaging of the target object can be realized, the data processing amount is small, the imaging precision is high and the method is easy to implement.

A vehicular sensing system includes at least one radar sensor disposed at a vehicle and having a field of sensing exterior of the vehicle. The radar sensor includes multiple transmitting antennas and multiple receiving antennas. The transmitting antennas transmit signals and the receiving antennas receive the signals reflected off objects. Multiple scans of radar data are received at an electronic control unit (ECU) and processed at a processor of the ECU. The ECU detects presence of a plurality of objects exterior the equipped vehicle and within the field of sensing of the at least one radar sensor. The ECU, responsive at least in part to processing at the processor of the received multiple scans of captured radar data and received vehicle motion estimation, tracks objects detected in the received multiple scans over two or more scans.

A preprocessing technique for synthetic radar images. An embodiment of a method for preprocessing synthetic aperture radar images includes: receiving range-compressed radar data generated from raw radar image data on-board a satellite or an airborne vehicle; generating a preliminary SAR image by performing a pre-focusing on the range-compressed radar data; extracting image subsectors from the preliminary SAR image; transmitting the extracted image subsectors to an on-ground portion; reconstructing the range-compressed radar data pertaining to the extracted image subsectors; and making the range-compressed radar data pertaining to the extracted image subsectors available for a Nominal synthetic aperture radar processor, wherein the Nominal synthetic aperture radar processor is configured to generate a focused SAR image having a nominal value of image resolution that is higher than the resolution of the preliminary SAR image.

Systems, methods, tangible non-transitory computer-readable media, and devices associated with sensor output segmentation are provided. For example, sensor data can be accessed. The sensor data can include sensor data returns representative of an environment detected by a sensor across the sensor's field of view. Each sensor data return can be associated with a respective bin of a plurality of bins corresponding to the field of view of the sensor. Each bin can correspond to a different portion of the sensor's field of view. Channels can be generated for each of the plurality of bins and can include data indicative of a range and an azimuth associated with a sensor data return associated with each bin. Furthermore, a semantic segment of a portion of the sensor data can be generated by inputting the channels for each bin into a machine-learned segmentation model trained to generate an output including the semantic segment.

A method comprising: obtaining an image; identifying a rotation angle for the image by processing the image with a first neural network; rotating the image by the identified rotation angle to generate a rotated image; classifying the image with a second neural network; and outputting an indication of an outcome of the classification, wherein the first neural network is trained, at least in part, based on a categorical distance between training data and an output that is produced by the first neural network.

The present disclosure discloses a microwave identification method, which is implemented on at least one device, including at least one processor and at least one storage device, the method including: the at least one processor obtains microwave data; the at least one processor generates an image of one or more objects based on the microwave data; the at least one processor obtains a model of each of the one or more objects; and based on the model of each of the one or more objects, the at least one processor identifies the one or more objects in the image of the one or more objects.

This document describes techniques for enabling de-aliased imaging for a synthetic aperture radar. Radar signals processed by a synthetic aperture radar (SAR) system may include false detections in the form of aliasing induced by grating lobes. The techniques described herein can reduce the adverse effects of grating lobes by obtaining an initial SAR image using a back-projection algorithm. Aliasing effects (e.g., false detections) in this initial image may be common due to the limitations of an SAR system moving at non-uniform speeds. A refined image is produced from the initial image by applying a de-aliasing filter to the initial image. The refined image may have reduced or eliminated false detections that attribute to aliasing effects, resulting in a better representation of the environment of the vehicle.

Examples of imaging systems are described herein which may implement microwave or millimeter wave imaging systems. Examples described may implement partitioned inverse techniques which may construct and invert a measurement matrix to be used to provide multiple estimates of reflectivity values associated with a scene. The processing may be partitioned in accordance with a relative position of the antenna system and/or a particular beamwidth of an antenna. Examples described herein may perform an enhanced resolution mode of imaging which may steer beams at multiple angles for each measurement position.