This paper proposes a coordinate-transform (CT) implementation for the fast factorized backprojection (FFBP) algorithm (CT-FFBP) to process high-resolution spotlight synthetic aperture radar (SAR) data. Unlike the FFBP utilizing two-dimensional image-domain interpolation for sub-aperture fusion, CT-FFBP finishes the image-projection using CT with the accommodation of chirp-z transform and circular shifting. Without interpolation, CT-FFBP yields enhanced efficiency over the interpolation based FFBP, besides maintaining high precision simultaneously. Both simulation and real-data experiments verifies the efficiency and precision superiorities of the CT-FFBP.

The multiple-input-multiple-output (MIMO) synthetic aperture radar (SAR) system with a linear antenna array can obtain 3D resolution. In practice, it suffers from both the translational motion errors and the rotational motion errors. Conventional single-channel motion compensation methods could be used to compensate the motion errors channel by channel. However, this method might not be accurate enough for all the channels. What is more, the single-channel compensation may break the coherence among channels, which would cause defocusing and false targets. In this paper, both the translational motion errors and the rotational motion errors are discussed, and a joint multichannel motion compensation method is proposed for MIMO SAR 3D imaging. It is demonstrated through simulations that the proposed method exceeds the conventional methods in accuracy. And the final MIMO SAR 3D imaging simulation confirms the validity of the proposed algorithm.

The multibaseline phase unwrapping (PU) of L 1 -norm can be efficiently solved using linear programming. However, the huge memory requirement of linear programming limits its application in multibaseline PU for large-scale data. In order to reduce the required memory when linear programming is performed, a novel mixed-norm multibaseline PU algorithm is proposed in this letter, which is regarded as an approximation of the L 1 -norm method. In this method, an L∞-norm cost function is employed to substitute for that of the L 1 -norm, i.e., it takes the optimization which is aimed to minimize the maximum component of the optimization variable as the representation of the one that minimizes the absolute sum of L 1 -norm. Consequently, the cost function in the proposed method changes to be an L 1 -norm plus an L ∞ -norm. Compared with the traditional L 1 -norm method, the size of the optimization variable in the proposed method is generally reduced by about one-seventh. Therefore, it is logical that less memory is needed in the proposed algorithm. The effectiveness of the proposed algorithm is validated via a simulated and real repeat-pass interferometric-synthetic-aperture-radar data set.

Multi-baseline phase unwrapping problem can be solved according to find the optimal solution of the L1-norm optimization. However, there are two problems: one is the huge memory required and the other is the difficulty in processing interferograms with severe noise. In order to decrease the memory requirement of the L1-norm method, with a cost function of L-norm is employed to approximate the L1-norm. Consequently, the objective function of the improved multi-baseline phase unwrapping is the form of L-norm+L1-norm, and the size of the new optimization variable is decreased by 57%. The performance of the proposed algorithm is validated via a real dataset with severe noise present, and the experiment demonstrates that the proposed algorithm not only presents a well phase unwrapping result of interferograms with good quality, but also performs a filtering against noise region.

This paper proposes a new algorithm for solving cross-range scaling for the inverse synthetic aperture radar (ISAR) imaging during a short Coherent Processing Interval (CPI) under a low Signal Noise Ratio (SNR). Based on the sparsity characteristic of the ISAR image, a Weighted Compressive Sensing (WCS) procedure is applied to generate high-resolution images, which can encourage signal components while suppressing noise. Then on the basis of the characteristics of 2-D Fourier transform (2-D FFT) and polar mapping, the Rotation Angle Velocity (RAV) initial estimation is realized by the correlation between two polar images. Finally, the maximum correlation position is found by using WCS, improving the estimation precision and efficiency of RAV. The rescaled ISAR image can be implemented. Both simulated and real-measured data confirm the feasibility and effectiveness of the proposed algorithm.