Inverse synthetic aperture radar (ISAR) images can be obtained using digital video broadcasting-terrestrial (DVB-T)-based passive radars. However, television broadcast-transmitted signals offer poor range resolution for imaging purposes, because they have a narrower bandwidth with respect to those transmitted by a dedicated ISAR system. To reach finer range resolutions, signals composed of multiple DVB-T channels are required. Problems arise, however, because DVB-T channels are typically widely separated in the frequency domain. The gaps between channels produce high grating Manuscript lobes in the image domain when Fourier-based algorithms are used to form the ISAR image. In this paper, compressive sensing theory is investigated to address this problem because of its ability to reconstruct sparse signals by using incomplete measures. By solving an optimization problem under the constraint of signal sparsity, passive ISAR images can be obtained with strongly reduced grating lobes. Both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

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Non-cooperative moving targets appear defocused within synthetic aperture radar (SAR) images and, in the case of ground targets, the blurring effect because of the uncompensated target motion decreases the radar's detection capabilities. Ground clutter, if sufficiently strong, may also obscure individual scatterers on moving targets resulting in a decreased ability to successfully classify the target. In this study, clutter suppression and inverse synthetic aperture radar (ISAR) imaging are combined to obtain high-resolution images of non-cooperative moving ground targets within SAR images. The clutter suppression technique proposed here is ISAR application oriented and is termed space-Doppler adaptive processing. Results obtained by processing a real dataset demonstrate the effectiveness of the proposed method.

The applicability of compressive sensing (CS) to radar imaging has been recently proven and its capability to construct reliable radar images from a limited set of measurements demonstrated. In this study, a common framework for inverse synthetic aperture radar (ISAR) imaging via CS is provided and a CS-based ISAR imaging method is proposed. The proposed method is tested for application such as image reconstruction from compressed data, resolution enhancement and image reconstruction from gapped data. The effectiveness of the proposed method is demonstrated on real datasets and the performance evaluated by means of image contrast

The applicability of Compressive Sensing (CS) to ISAR has been widely discussed in the last few years. In particular, CS based ISAR image reconstruction algorithms have been developed and their effectiveness proven when dealing with incomplete ISAR data. Resolution enhancement has also been identified as a case for which CS can be effectively applied to ISAR imagery. In this case, the acquired signal can be interpreted as an incomplete data in the frequency/slow-time domain and CS used to reconstruct the super-resolved ISAR image. In this paper, an exhaustive performance analysis is carried out also as a comparison among CS and conventional super resolution techniques. Several concepts and methods are introduced in order to define effective performance analysis that is not simply based on a visual inspection.

Moving target often appear defocussed in SAR images due to their relative motion with respect to the SAR center scene. In several applications that require target classification and/or recognition an unfocussed image of a target would likely lead to misclassification, therefore decreasing classification performances. In this paper, the authors present a new method for refocussing moving targets starting from formed Single Look Complex (SLC) SAR images. Results obtained by applying the proposed techique to Cosmo Skymed (CSK) SLC SAR images show its effectiveness.

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Slow moving ground targets are invisible within synthetic aperture radar (SAR) images since they appear defocused and their backscattered signal completely overlap the focused ground return. In order for this targets to be detected and refocused the availability of some spatial degrees of freedom is required. This allows for space/slow time processing to be applied to mitigate the ground clutter. However, multichannel SAR (M-SAR) systems are very expensive and the requirements in terms of baseline length can be very restrictive. In this study a processing scheme that exploits high PRF single channel SAR system to emulate a multichannel SAR is presented. The signal model for both target and clutter components are presented and the difference with respect to an actual M-SAR are highlighted. The effectiveness of the proposed processing is then demonstrated on simulated a measured dataset