Standard focusing of data from synthetic aperture radar (SAR) assumes a straight recording track of the sensor platform. Small nonlinearities of airborne platform tracks are corrected for during a motion-compensation step while maintaining the assumption of a linear flight path. This paper describes the processing of SAR data acquired from nonlinear tracks, typical of sensors mounted on small aircraft or drones flying at low altitude. Such aircraft do not fly along straight tracks, but the trajectory depends on topography, influences of weather and wind, or the shape of areas of interest such as rivers or traffic routes. Two potential approaches for processing SAR data from such highly nonlinear flight tracks are proposed, namely, a patchwise frequency-domain processing and mosaicking technique and a time-domain back-projection-based technique. Both are evaluated with the help of experimental data featuring tracks with altitude changes, a double bend, a 90 circ curve, and a linear flight track. In order to assess the quality of the focused data, close-ups of amplitude images are compared, impulse response functions of a point target are analyzed, and the coherence is evaluated. The experimental data were acquired by the German Aerospace Center's E-SAR L-band system.

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A novel method for moving-target tracking using single-channel synthetic aperture radar (SAR) with a large antenna beamwidth is introduced and evaluated using a field experiment and real SAR data. The presented approach is based on subaperture SAR processing, image statistics, and multitarget unscented Kalman filtering. The method is capable of robustly detecting and tracking moving objects over time, providing information not only about the existence of moving targets but also about their trajectories in the image space while illuminated by the radar beam. We have successfully applied the method on an experimental data set using miniature SAR to accurately characterize the movement of vehicles on a highway section in the radar image space. > Moving target tracking in single-channel, wide-beam SAR < 1 Abstract-A novel method for moving target tracking using single-channel Synthetic Aperture Radar (SAR) with a large antenna beam width is introduced and evaluated using a field experiment and real SAR data. The presented approach is based on sub-aperture SAR processing, image statistics and multitarget unscented Kalman filtering. The method is capable of robustly detecting and tracking moving objects over time, providing information not only about the existence of moving targets but additionally about their trajectories in the image space while illuminated by the radar beam. We have successfully applied the method on an experimental data set using MiSAR to accurately characterize the movement of vehicles on a highway section in the radar image space.

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This data set is the first-of-its-kind spatial representation of multi-seasonal, global C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures. Coverage comprises land masses and ice sheets from 82° Northern to 79° Southern latitudes. The data set is derived from multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 C-band SAR images acquired in Interferometric Wide-Swath Mode from 1-Dec-2019 to 30-Nov-2020. The data set encompasses three sets of seasonal (December-February, March-May, June-August, September-November) metrics produced with a pixel spacing of three arcseconds: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-days repeat-pass coherence at VV or HH polarizations, 2) Mean radiometrically terrain corrected backscatter (γ0) at VV and VH, or HH and HV polarizations, and 3) Estimated parameters of an exponential coherence decay model. The data set has been produced to obtain global, spatially detailed information on how decorrelation affects interferometric measurements of surface displacement and is rich in spatial and temporal information for a variety of mapping applications.