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In this paper we develop an efficient anti-noise imaging algorithm based on Bayesian Compressive Sensing (BCS) theory. Random sampling is applied in range and azimuth direction respectively, and sparse dictionary matrixes are designed independently in each direction according to imaging geometry model. At last, BCS theory is used to reconstruct SAR image. BCS theory takes the prior knowledge of targets...
This paper presents a method for imaging of moving targets via the compress sensing by treating the imaging as a problem of signal representation in an over-complete dictionary. The essential idea behind sparse signal representation models comes from the fact that SAR ground moving targets are sparsely distributed in the observation scene and the received SAR echo is decomposed into the sum of basis...
The paper proposes an efficient mathematical description for the range model of a moving target in synthetic aperture radar (SAR) geometry, without any limitations on the model order. The target may have high-order motions such as accelerations, jerks or micro-motions, and any-order one is decomposed in terms of azimuthal and radial directions. The relationship between target motion component, phase...
This paper presents a SAR raw data simulation approach using the inverse frequency scaling algorithm (IFSA) based on deramp processing. It is time-saving and can take motion error into account. The limitation of the inverse chirp scaling algorithm (ICSA) on transmitted waveforms is also overcome. Simulated results show the validity of the algorithm.
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