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Due to uncertainties of the platform motion, the trajectory of airborne millimeter-wave interferometric synthetic aperture radar (MMW-InSAR) usually demonstrates highly non-linear, which cause dynamic baseline to height inversion. In such cases, traditional InSAR methods may suffer from serval difficulties for imaging and height inversion. In this paper, a novel back projection imaging algorithm via...
Due to trajectory complexity of circular synthetic aperture radar (CSAR), it is very difficult to compensate motion errors accurately using conventional frequency-based SAR autofocusing approaches. With point-by-point coherent integration, autofocus back projection (ABP) algorithm is promising for motion error correction in the case of complex trajectory. However, the exiting ABP methods usually assume...
In the field of synthetic aperture radar (SAR) imaging, imaging platform is usually in a linear motion; SAR imaging and platform's height measurement are separated. However, the real platforms, such as spin platform, are in complex movement. For these problems, imaging feasibility of a novel spin platform 3D SAR imaging is researched and this imaging result is used to measure platform's height. Firstly,...
The 3-D image of Linear array synthetic aperture radar (LASAR) usually exhibit high sparseness, so sparse imaging algorithms based on compressed sensing (CS) theory can be used for LASAR 3-D imaging. However, the conventional CS-based imaging scheme suffers from huge computational time, especially for large scene imaging, which requires a huge sensing matrix to reconstruct the whole scene. In this...
Linear array SAR (LASAR) is a promising 3-D radar imaging technology. As 3-D radar images usually exhibit strong sparsity, compressed sensing sparse recovery algorithms can be used for LASAR imaging even if the echoes are under-sampled. However, most of the existing sparse recovery algorithms assume exact knowledge of the signal acquisition model, which is impractical for LASAR due to the phase errors...
Compressed sensing (CS) has been receiving great interests as it can reconstruct sparse signals exactly from a very small number of samples with high probability. In this paper, we demonstrate that “one-active” linear array SAR imaging can be formulated as sparse reconstruction problem and analyses the question of scattering center estimation in “one-active” linear array SAR via CS technique. According...
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