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 90deg 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.

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.

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.

MEMPHIS is an experimental millimeter-wave synthetic aperture radar (SAR) system that acquires cross-track multibaseline interferometric data at high resolution in a single pass, using four receive horns. In this paper, we present the SAR system and navigation data, and propose a processing chain from the raw data input to a digital surface model (DSM) output. This processing chain includes full bandwidth reconstruction of the steppedfrequency SAR data, azimuth focusing with an Extended Omega-K algorithm, generation ofinterferograms for each available baseline, phase unwrapping using the multibaseline data, and phaseto-height conversion. The hardware and processing chain were validated through the analysis of experimental Ka-band data. The SAR image resolution was measured with point targets and found to be ~2% and ~15% coarser than the theoretical value in range and azimuth, respectively. The geolocation accuracy was typically better than 0.1 m in range and 0.2 m in azimuth. Observed depression angledependent interferometric phase errors were successfully removed using a correction function derived from the InSAR data. Investigation of the interferometric phase noise showed the utility of a multibaseline antenna setup; the number of looks and filter size used for the DSM generation were also derived from this analysis. The results showed that in grassland areas, the height difference between the ~2 m-resolution InSAR DSMs and the reference ALS models was 0 ± 0.25 m.

It has been shown using simulated data that phase estimation of cross-track multibaseline synthetic aperture radar (SAR) interferometric data was most efficiently achieved through a maximum likelihood (ML) method. In this paper, we apply and assess the ML approach on real data, acquired with an experimental Ka-band multibaseline system. Compared to simulated data, dealing with real data implies that several calibration steps be carried out to ensure that the data fit the model. A processing chain was, therefore, designed, including steps responsible for compensating for imperfections observed in the data, such as beam elevation angle dependent phase errors or phase errors caused by imperfect motion compensation. The performance of the ML phase estimation was evaluated by comparing it to results based on a coarse-to-fine (C2F) algorithm, where information from the shorter baselines was used only to unwrap the phase from the longest available baseline. For this purpose, flat areas with high coherence and homogeneous texture were selected in the acquired data. The results show that with only four looks, the noise level was marginally better with the C2F approach and contained fewer outliers. However, with more looks, the ML method consistently delivered better results: noise variance with the C2F approach was slightly but steadily larger than the variance obtained with ML method.