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