| Summary: | For downward-looking linear array (DLLA) three-dimensional (3-D) synthetic aperture radar (SAR), it is necessary to realize the super-resolution in both azimuth and cross-track direction due to the limited lengths of the synthetic aperture and the linear array. As all the scatterers are assumed on the uniform grids, the cross-track super-resolution can be achieved by 1-D compressed sensing. In the real imaging system, however, the gridding error should be considered because the biased scatterers lead to the mismatch of the measurement matrix and affect the imaging performance. The 1-D mismatch in cross-track direction has been solved by atomic norm minimization and off-grid sparse Bayesian inference. With the development of the super-resolution methods, the 2-D super-resolution in both azimuth and cross-track direction is realized by the 2-D compressed sensing (CS) algorithms. To solve the 2-D mismatch problem, a novel 2-D mismatch compensation method for DLLA 3-D SAR is proposed. Instead of converting the 2-D matrix signals to the 1-D vectors, the proposed method directly processes the 2-D mismatch with 2-D joint model. Furthermore, the 2-D joint model with 2-D mismatch is simplified as a normal sparse linear model, which is suitable for most of the CS reconstruction algorithms. It can not only provide better reconstruction performance but also reduce the memory cost and computation load. Finally, the simulation experiments are shown to demonstrate the validity of the proposed method.
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