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.
Passive bistatic inverse synthetic aperture radar (PB-ISAR) has been recently introduced to add an important capability to passive coherent location (PCL) systems. Although evidence of such capability has been provided, a theoretical background that supports such findings is needed to fully comprehend PB-ISAR imaging. This paper provides a full theoretical basis for PB-ISAR including a performance analysis in terms of spatial resolution. Examples with real data are also provided as case studies
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