Recently, compressed sensing (CS) has been applied in synthetic aperture radar (SAR). A framework of mixed sparse representation (MSR) has been proposed for reconstructing SAR images due to the complicated ground features. The existing method decomposes the image into the point and smooth components, where the sparse constraint is directly applied to the smooth components. This makes it difficult to tackle the complex-valued SAR images, since the phase angles of SAR images are always stochastic. A magnitude-phase separation MSR method is proposed for CS-SAR imaging based on approximated observation. Compared to the existing method, the proposed method has better reconstruction ability, because it only imposes the sparse constraint on the magnitude of the smooth components, and therefore, the phase angles are still stochastic. Furthermore, owing to the inherent low memory requirement of approximated observation, the proposed method requires much less memory cost. In the simulation and experimental results, the proposed method deals with the complex-valued SAR images effectively and demonstrates superior performance over the chirp scaling algorithm and the existing MSR method.
Compressive sensing (CS) theory has achieved significant success in the field of synthetic aperture radar (SAR) imaging. Recent studies have shown that SAR imaging for sparse scene can also be successfully performed with 1-bit quantized data. Existing reconstruction algorithms always involve large matrix-vector multiplications which make them much more time and memory consuming than traditional matched filtering (MF) -based focusing methods because the latter can be effectively implemented by FFT. In this paper, a novel CS approach named BCS-AO for SAR imaging with 1-bit quantized data is proposed. It adopts the approximated SAR observation model deduced from the inverse of MFbased methods and is solved by an iterative thresholding algorithm. The BCS-AO can handle large-scaled data because it uses MF-based fast solver and its inverse to approximate the large matrix-vector multiplications. Both the simulated and real data are processed to test the performance of the novel algorithm. The results demonstrate that BCS-AO can perform sparse SAR imaging effectively with 1-bit quantized data for large scale applications.
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