With the advancement of Microwave Photonic (MWP) synthetic aperture radar (SAR) technology, resolution has increased to 0.02 m, and platforms have expanded from airborne to vehicle-borne. Incorporating ultra-wideband, long synthetic aperture, and varied observation ranges presents two primary challenges for MWP SAR imaging: 1) The enhancement of two-dimensional (2-D) resolution renders the imaging process more susceptible to 2-D space-variant motion errors (SVMEs). 2) The expansion of application platforms, particularly close-range observation by vehicle-borne platforms, invalidates traditional imaging algorithms based on the far-field assumption. To address the challenges, a novel time-domain processing framework is proposed for both airborne and vehicle-borne MWP SAR systems. Firstly, we analyzes wavenumber spectrum resampling during the back-projection (BP) process, establishing a mapping relationship between phase errors in image and time domain. This allows for the estimation of trajectory deviation, enabling a rough estimation of the 2-D SVME. Subsequently, a motion compensation (MoCo) method, based on an overlapping sub-image configuration combined with fast ground Cartesian BPA (GCBPA), is introduced to enable imaging. This method solves the problem that MoCo method cannot be integrated with fast GCBPA. In the third stage, the relationship between azimuth phase error (APE) and 2-D phase error is established. Leveraging this relationship, a 2-D wavenumber domain autofocus method is developed to concurrently compensate for APE and nonsystematic range cell migration (NsRCM). Experimental validations on both airborne (0.03m) and vehicle-borne (0.02m) MWP SAR platforms data confirm the effectiveness and versatility of the proposed time-domain processing framework.

In recent years, the ground Cartesian back-projection (GCBP) algorithm has demonstrated significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometries and complex configurations, primarily due to its interpolation-free operation. However, airborne BFSAR systems must additionally address motion error compensation challenges. Implementing effective motion compensation (MoCo) within the GCBP framework for BFSAR presents two key challenges: 1) The forward-looking configuration induces severe image spectrum tilt and nonsystematic range cell migration (NsRCM), significantly degrading phase error estimation accuracy; and 2) Spectrum resampling during BP processing obstructs direct time domain phase error (TDPE) estimation from the image. To address these challenges, this paper proposes a phase error reverse recovery method based on ground combined beam coordinate (GCBC) for BFSAR. The proposed MoCo method establishes the GCBC system aligned with the echo signal’s azimuth Doppler variation, which eliminates image spectrum tilt and reduces NsRCM caused by the bistatic configuration. Within this system, we introduce the spectrum center correction and spectrum tilt correction, which effectively remove image spectrum aliasing and enable accurate image domain phase error (IDPE) estimation. Furthermore, an analytical reverse recovery relationship between IDPE and TDPE is derived. These stages significantly enhance the accuracy and robustness of BFSAR motion error estimation. Simulation and real data results demonstrate the proposed method’s superior performance.

In recent years, the back-projection (BP) algorithm has shown good potentials in SAR focusing with nonideal conditions like nonideal trajectory and nonuniform sampling. However, when applying the original BP algorithm directly to periodic non-uniform sampling data, ghostings can be introduced into the imaging results, leading to image quality distortion. To address this issue, a novel reconstruction method based on joint sub-channel BP images is proposed. We first analyze the causes and properties of Doppler grating lobes and BP ghostings in the periodic non-uniform signals. Based on the analysis, a reconstruction process of the joint sub-channel BP images is established to achieve the purpose of suppressing ghosting. The proposed method can be smoothly combined with the Ground Cartesian back-projection (GCBP) algorithm to realize high computational efficiency. Finally, the simulation and measured data are presented to verify the effectiveness of the algorithm.

The demand for high-resolution and wide-swath (HRWS) for squint azimuth multichannel synthetic aperture radar (MSAR) platform is increasingly urgent. However, the existence of interference will seriously contaminate the squint MSAR imagery; in particular, the deceptive forwarding interference (DFI) produced by the digital radio frequency memory (DRFM) technology makes the synthetic aperture radar (SAR) imagery confusing. For this point, the research on anti-DFI of squint MSAR is discussed in detail in this article. The presence of the Doppler ambiguity of the signal increases the complexity and difficulty of the DFI suppression. To solve this problem, the difference in the space–time spectrum between the valid signal and the DFI is first analyzed, and the steering vectors of each component in the echo are mined as prior information. Based on the prior information, a two-step processing is performed: the first step is to suppress the main-lobe DFI by using subspace projection and the second step is to suppress sidelobe DFI and complete signal spectrum reconstruction with the multiple Doppler direction linearly constrained minimum variance (MDD-LCMV) beamformer. Finally, the two experiment results show the excellent performance of the proposed method in squint MSAR for suppressing DFI.

When synthetic aperture radar (SAR) works in high-squint (HS) mode, the interpolation and scaling operation of traditional frequency domain imaging algorithms will change the original structure of motion error, and lead to imaging difficulties. As a classical time-domain imaging algorithm, the back-projection (BP) algorithm is linear processing with a high tolerance for motion error. Therefore, the BP algorithm is very suitable for HS SAR imaging. To further improve the estimation accuracy of motion error, an innovative affine coordinate (AC) system is introduced into the BP algorithm. Based on this AC system, a novel 2-D autofocus algorithm is proposed, which can more accurately estimate and correct the 2-D phase error of the HS SAR BP image. The proposed algorithm has the following advantages: 1) the AC imaging grid is established according to the proposed resolution calculation method based on the BP image spectrum. Under this imaging grid, the nonsystem range cell migration (NsRCM) and the range defocus term of the BP image are significantly reduced, making the phase error estimation more accurate; 2) a spectrum alignment processing for BP image in the AC system is proposed to remove the spectrum aliasing so that the azimuth phase error (APE) can be accurately estimated; and 3) the spectrum of the BP image is orthogonal in the AC system, which makes the 2-D phase error compensation based on the established prior phase error structure more accurate. Simulation and real data experiments validate the performance of the proposed algorithm.

The Ground-Cartesian factorized back-projection (G-CFBP) is an efficient time-domain processing algorithm without image interpolation, and can realize accurate imaging for curved trajectory synthetic aperture radar (SAR). Its superiority shows good potential in airborne circular SAR (CSAR) imaging. However, the motion compensation (MoCo) based on ground Cartesian back-projection (GCBP) in the airborne CSAR is still a challenge. There are two main problems: one is that the existence of the image spectrum aliasing makes the processing of the phase error estimation inaccurate; the other is that the mapping relationship of the phase error between the image spectrum domain and the azimuth time domain still needs to be studied within GCBP processing chain. To tackle the above two problems, a novel MoCo method applicable to the GCBP algorithm is proposed and can be mainly divided into two steps: the first step is to remove the sub-aperture image spectrum aliasing by a spectrum compression operation; the second step is to establish an analytical phase error structure, which includes an auto-selection criterion of the effective support region for GCBP image. The first step ensures the accuracy of the phase error estimation, and the second step establishes the inverse-mapping relationship of the phase error between the image spectrum and azimuth time. These two procedures are both vital in improving the accuracy and robustness of the GCBP-based MoCo for the CSAR imaging. The processed results of simulated and real data are provided to verify the effectiveness of the proposed method.