A method and a system for detection and synthetic aperture (SA) imaging of a target are disclosed. The method may include illuminating a scene with a search signal transmitted from a moving platform, receiving a search return signal from a target present in the scene, and estimating, from the search return signal, the range and the angular location of the target. The method may also include generating an SA transmission signal and a local oscillator (LO) signal with a time delay therebetween based on the estimated range, and illuminating the scene with the SA transmission signal pointed along an imaging direction based on the estimated angular location of the target. The method may further include receiving an SA return signal from the target, mixing the SA return signal with the LO signal to generate SA signal data, and generating an SA image of the target from the SA signal data.

The disclosure concerns a method for determining a change in position and orientation of an object using data from a synthetic aperture radar (SAR) with the following steps: acquiring a radar image containing the object; dividing the radar image into at least two subimages; acquiring short radar information comprising a first reflected pulse, which has been recorded by the radar and/or a quantity derived therefrom, the first reflected pulse having information about image data of the radar image; for the short radar information, performing an autofocus method which determines a change in distance for each of the at least two subimages; and determining the change in position and orientation of the object given for each point of the object by the respective change in distance of the corresponding subimage.

The present disclosure relates to a method for enhancing sematic features of SAR image oriented small set of samples, comprising: acquiring a sample set of an SAR target image, and performing transfer learning and training on the sample set to obtain a initialized deep neural network of an SAR target image, the sample set comprising an SAR target image and an SAR target virtual image; performing network optimization on the deep neural network by an activation function, and extracting features of the SAR target image by the optimized deep neural network to obtain a feature map; and mapping, by an auto-encoder, the feature map between a feature space and a semantic space to obtain a deep visual feature with an enhanced semantic feature.

A sensing system for a vehicle includes at least one radar sensor disposed at the 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 sensed by the radar sensor are received at a control, and a vehicle motion estimation is received at the control. The control, responsive to received scans of sensed radar data, detects the presence of objects within the field of sensing of the radar sensor. The control, responsive to the received scans of sensed radar data and the received vehicle motion estimation, synthesizes a virtual aperture and matches objects detected in the scans and determines a separation between detected objects by tracking the detected objects over two or more scans.

A method for detecting radial deformation in a winding of a transformer may include synthetic aperture radar (SAR) imaging of the winding using ultra high frequency (UHF) electromagnetic signals in a first instance of the winding to obtain a first image of the winding; SAR imaging of the winding using UHF electromagnetic signals in a second instance of the winding to obtain a second image of the winding; and comparing the first image of the winding and the second image of the winding to detect a radial deformation in the winding. The UHF electromagnetic signals may be transmitted as a plurality of successive sinusoidal signals, where frequencies of the successive sinusoidal signals gradually change from a first frequency to a second frequency.

A system for determining a location in a disadvantaged signal environment includes three aerial vehicles hovering at high altitude and spaced apart to form a triangle, and a mother aerial vehicle positioned a distance away and at a lower altitude. The mother aerial vehicle acquires and transmits coarse geolocation information, using a pulse compression, high-power X Band radar and directional antenna, to each of the three aerial vehicles to direct them to coarse geo-positions above designated respective ground locations. One of the three aerial vehicles has a synthetic aperture radar for producing a terrain strip-map that is mensurated against a map database to provide fine position adjustments for each of the three aerial vehicles, which are also also configured to transmit a respective signal coded with its latitude, longitude, and altitude, for a computing device to perform time difference of arrival measurements of the signals to determine its location.

A radar imaging system to reconstruct a radar reflectivity image of a scene including an object moving with the scene, includes an optical sensor to track the object over a period of time including multiple time steps to produce, for each of the multiple time steps, a deformation of a nominal shape of the object, and an electromagnetic sensor to acquire snapshots of the scene over the multiple time steps to produce a set of radar reflectivity image of the object with deformed shapes defined by the corresponding deformations of the nominal shape of the object. The system also includes a processor configured to determine, for each of the multiple time steps using the deformation determined for the corresponding time step, a transformation between the radar reflectivity image of the object acquired by the electromagnetic sensor at the corresponding time step and a radar reflectivity image of the object in the prototypical pose, and to combine the radar reflectivity images of the object with deformed shapes transformed with the corresponding transformations to produce the radar reflectivity image of the object in the prototypical pose.

Disclosed is a calibration method of performing dual radiometric compensation by using an antenna gain pattern of a synthetic aperture radar (SAR) both in a time domain and in a frequency domain. The method may include performing frequency-domain radiometric compensation in relation to an elevation angle and performing time-domain radiometric compensation in relation to a frequency to calibrate the antenna gain pattern.

A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.

A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.