To obtain high resolution and wide swath (HRWS) image, the azimuth multi-channel technique has been widely used in synthetic aperture radar (SAR) systems to overcome the contradiction between wide swath and high pulse repetition frequency (PRF). For high image quality, channel mismatch correction is an essential step in multi-channel SAR data imaging. However, in the case of airborne multi-channel SAR, motion errors will severely degrade the performance of channel mismatch correction. To deal with this problem, this paper proposes an improved airborne multi-channel SAR imaging method with motion compensation (MOCO) and range-variant channel mismatch correction. Firstly, motion errors are compensated based on resampling and phase compensation. Then the time delay error and constant gain-phase error between channels are estimated and corrected, followed by range-variant phase error correction based on a novel range-down-sampling method, which reduces the influence of motion errors on channel mismatch correction significantly. Finally, simulated and real data processing results are used to demonstrate the effectiveness of the proposed method.

For azimuth multi-channel synthetic aperture radar (SAR), unavoidable inconsistency errors between channels can degrade SAR image quality severely, leading to possible ghost targets and image defocusing, etc. To address this issue, a novel channel inconsistency estimation method is proposed based on maximum normalized image sharpness. First, channel amplitude and time delay errors are corrected in the coarse compensation step. Then images of each channel are attained by azimuth spectrum recovery and imaging processing. Next, range-variant channel phase errors are estimated via optimizing normalized image sharpness, which reaches the maximum value when the image is focused well or ghost targets are suppressed completely. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is employed to get the optimal solution based on the derived gradient of objective function. Finally, the ultimate image is formed through adding up phase compensated images of each channel. By optimizing the focused image quality, the proposed algorithm achieves high estimation accuracy. Simulated data and real multi-channel SAR data are processed to demonstrate the effectiveness of the proposed method.

Rotating ships usually suffer from complicated defocusing in high-resolution spaceborne synthetic aperture radar (SAR) imagery due to the non-uniform motions, which severely restricts the classification and interpretation of ships in SAR applications. In this paper, a novel refocusing method is proposed to improve the imaging quality of rotating ships by postprocessing SAR imagery. We first attempt to utilize the nonlinear chirp scaling (NLCS) principle to correct space-variant phase errors induced by target rotations. Specifically, an azimuth inverse compression approach without zero-padding operation is designed to recover the azimuth frequency modulation (FM) of the target image data. Then, a modified NLCS operation is derived for space-variant phase error correction by introducing a high-order perturbation function. An objective function based on image quality is also constructed to estimate the optimal perturbation parameters. Finally, simulated data and real spaceborne SAR data are processed to demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques.

Operating multi-channel synthetic aperture radar (SAR) in ScanSAR mode enables an ultrawide-swath and high-resolution imagery. A critical step in multi-channel SAR data processing is to accurately estimate and compensate phase inconsistency between channels. However, the Doppler spectrum distribution of ScanSAR relies heavily on the characteristic of illuminated regions, which may make the traditional time-domain phase estimation method failure under nonuniform scenes. In order to ensure that the performance of phase estimation is robust with respect to the variation of scenes, an innovative scene adaptive phase inconsistency estimation method for the ScanSAR system is presented in this letter, which can estimate channel phase error more accurately by extracting scene-center frequency from received data. The performance of the presented approach is evaluated using real airborne multi-channel SAR data. Experimental results show that the modified phase inconsistency estimation method is more effective and adaptable to different scenes compared with the conventional method.

High-resolution images of urban areas generated by spaceborne synthetic aperture radar (SAR) have a wide range of applications. However, with the improvement in resolution, the slant range error caused by the height of buildings leads to high-order and complex phase errors during the imaging process, which in turn causes image defocusing. To address this issue, an optimization algorithm is proposed based on the conjugate gradient method to estimate and compensate for the phase error. It uses image entropy as the optimization objective function, which is applicable to segmented images, effectively solving the problem of variation of phase error at different heights. In addition, the algorithm improves the computational efficiency by introducing a restart strategy and a momentum term in the one-dimensional line search. Experimental results using both simulation and actual SAR data show that the proposed algorithm outperforms the existing methods in terms of both efficiency and focusing quality.