Spaceborne bistatic synthetic aperture radar (SAR) system with an inclined geosynchronous (GEO) illuminator and a low-earth-orbit (LEO) receiver is capable of providing a vast area of surveillance and fine spatial resolution, which presents huge potentials for future earth observation. In this letter, inclined GEO–LEO azimuth multichannel SAR (MC-SAR) system for high-resolution wide-swath imaging is investigated. Starting from modeling geometry of inclined GEO–LEO bistatic MC-SAR, the signal model is analyzed in detail for the first time, and the equivalent positions of the received channels are obtained. Then, we found the spatial-variant residual phase error cannot be compensated accurately by conventional effective phase center (EPC) processing. Moreover, because of the complex bistatic configuration, the frequency-domain imaging algorithms become extremely difficult to achieve a well-focused and phase-preserved image. Meanwhile, the time-domain back-projection algorithm (BPA) faces with a technical challenge due to the nonuniform sampling in azimuth. To address these issues, a bistatic weighted BPA (BWBPA) for GEO–LEO MC-SAR is derived and presented. Without the procedure of EPC, the BWBPA can suppress azimuth ambiguities effectively and yield phase-preserved SAR images. Simulated data results show the validity of the presented method.

This letter presents a new approach of digital elevation model generation using circular synthetic aperture radar. The approach is composed of two steps, target separation and height estimation. In step 1, subaperture images are reshaped into vectors and stacked to build a composite matrix. The composite matrix is decomposed into a low-rank matrix and a sparse matrix based on “Semi-Soft Go Decomposition” algorithm. The targets, whose height is equal to the reference imaging height plane, are contained in the low-rank matrix, whereas the targets with other heights are contained in the sparse matrix. In step 2, the sparse matrix is further decomposed into several shifted low-rank matrices, each of which corresponds to the targets sharing one height, based on “Shifted Subspaces Tracking” algorithm. The height of the targets contained in each low-rank matrix is estimated from the shifts of the matrix's subspaces. The effectiveness of the proposed approach is investigated by the Gotcha public release data set.

Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method is proposed to design transmitting and receiving array elements, which increases the minimum spacing of the antenna by sacrificing several equivalent phase centers. In MIMO arrays, the minimum antenna spacing in the azimuth direction is doubled, which increases the variety of antenna options for this design. To improve the accuracy of the system, a new method is proposed to estimate channel phase errors, amplitude errors, and position errors. The position error is decomposed into three directions with one compensated by the phase error and two estimated by the strong point. Finally, we validate the accuracy of the system and our error estimation method through simulations and experiments. The results prove that the GB-SAR system performs well in bridge deformation and vibration monitoring with the proposed method.

Devolution technique and linear inversion can be utilized in forward-looking imaging radar. However, complicated forward-looking geometry with curve trajectory for high-speed dynamic platform makes the traditional model mismatched, and the conventional method inefficient. In this paper, a superresolution forward-looking imaging algorithm based on extended sparsity and step-size adaptive matching pursuit (ESSAMP) under the optimized Doppler convolution model is proposed. The main contributions are that the multi-domain cascade phase factor (MCPF) is constructed to compensate for unified range cell migration introduced by complicated forward-looking geometry and high-order phase introduced by three-dimensional (3-D) acceleration. Then, the optimized Doppler convolution model for forward-looking imaging is derived. The modified over-complete dictionary with Doppler phase information is then fully exploited to perform the Doppler deconvolution. Besides, the forward-looking imaging problem is solved by the proposed ESSAMP method. Numerical simulations validate that the proposed algorithm is effective to reconstruct forward-looking scenarios, robust to noise, and efficient to implement.

In multi‐channel high‐resolution and wide‐swath (HRWS) synthetic aperture radar (SAR) processing, range sampling time errors and phase mismatches severely degrade the reconstruction performance of unambiguous Doppler spectrum. To address this problem, an improved channel mismatch estimation algorithm for multi‐channel HRWS SAR is presented in this Letter. The technique of combining phase wrapping and weighted least‐squares fitting is capable of realising a robust estimation of range sampling time errors and constant phases between adjacent channels. Then, Doppler centroid as well as channel phase mismatches can be estimated from these constant phases based on the theory of spatial cross‐correlation coefficient (SCCC). Compared to the conventional algorithm, the improved performance can be achieved by using the proposed two‐step procedure. The effectiveness of the proposed algorithm is confirmed by experimental results based on airborne real data.

3D scene reconstruction from single‐pass circular synthetic aperture radar (CSAR) data has been a research hot spot in various remote sensing applications. Among existing literature, radargrammetry techniques and model assistant methods are two dominant approaches. However, the performance of these methods is limited in practical applications: radargrammetry techniques obtain merely one height value along vertical; model assistant approaches only work when valid models are given. A single‐pass CSAR 3D scene point cloud reconstruction method is proposed. First, the connection between an object and its projections in multi‐aspect SAR images is established with no auxiliary model. On the basis of the connection, voxels are classified into inner voxels (voxels beneath the target profile) and outside voxels (voxels outside of the target profile). Discarded all the outside voxels, the remained inner voxels are combined to form the 3D point cloud of the target. It is similar to the sculpturing process from a raw material to a piece of craft. Finally, a 3D scene is retrieved through assembling all reconstructed targets into a unified coordinate system. The feasibility of the proposed method is validated by the Gotcha Volumetric SAR Data Set.