As a crucial sensor for wide-area Earth observation, spaceborne synthetic aperture radar (SAR) plays a pivotal role in large-scale terrain mapping, ocean observation, disaster monitoring, and so forth. Driven by the increasing demands for diverse applications, enhanced performance, and the continuous advancement of satellite and radar technologies, the distributed configuration has emerged as a key developmental trend for spaceborne SAR. This review comprehensively summarizes the systems and typical applications of distributed spaceborne SAR. The system configurations encompass homogenous distributed SAR, formed by multiple identical or similar platforms, and heterogeneous distributed SAR, characterized by significant differences between the transmitting and receiving platforms. Typical applications of distributed SAR include intelligent target recognition, terrain mapping, deformation retrieval, atmosphere measurement, and ocean observation, among others. Finally, the review offers a prospective outlook on the future development of distributed spaceborne SAR.

Global navigation satellite system (GNSS)-based synthetic aperture radar interferometry (InSAR) employs GNSS satellites as transmitters of opportunity and a fixed receiver with two channels, i.e., direct wave and echo, on the ground. The repeat-pass concept is adopted in GNSS-based InSAR to retrieve the deformation of the target area, and it has inherited advantages from the GNSS system, such as a short repeat-pass period and multi-angle retrieval. However, several interferometric phase errors, such as inter-channel and atmospheric errors, are introduced into GNSS-based InSAR, which seriously decreases the accuracy of the retrieved deformation. In this paper, a deformation retrieval algorithm is presented to assess the compensation of the interferometric phase errors in GNSS-based InSAR. Firstly, the topological phase error was eliminated based on accurate digital elevation model (DEM) information from a light detection and ranging (lidar) system. Secondly, the inter-channel phase error was compensated, using direct wave in the echo channel, i.e., a back lobe signal. Finally, by modeling the atmospheric phase, the residual atmospheric phase error was compensated for. This is the first realization of the deformation detection of urban scenes using a GNSS-based system, and the results suggest the effectiveness of the phase error compensation algorithm.

The performance of adaptive beamforming will degrade dramatically in practical application due to system errors including signal direction error, array geometry error, gain, and phase errors. Besides, the performance will further deteriorate when the desired signal is contained in the sample data. Therefore, a robust adaptive beamforming method based on the covariance matrix reconstruction with annular uncertainty set (AUS) and vector space projection (VSP) is proposed in this letter. By integrating the corresponding Capon spectrum over the surface of AUS, the interference-plus-noise covariance matrix (INCM) and the desired signal covariance matrix are reconstructed, respectively. Because the steering vector (SV) of desired signal lies in the intersection of the signal subspaces of the sample covariance matrix and the reconstructed desired signal covariance matrix, it can be estimated by the VSP method. Finally, the adaptive weight vector is calculated based on the reconstructed INCM and the estimated SV. Simulation and experiment results show that the proposed method can effectively suppress the interference and achieve excellent performance under system errors.

A multiple-input multiple-output (MIMO) radarwith widely separated antennas on moving platforms suffers from the effects of platform deviations on the target parameter estimation since the trajectories of the moving platforms are sensitive to environmental factors, such as strong wind. This article addresses the joint estimation of multiple target positions and velocities as well as radar system deviations to minimize the impact of platform deviations. The proposed algorithm can also be regarded as a self-calibration technique. First, the grid search dimensions for parameter estimation are reduced via a generalized maximum likelihood (GML) algorithm. Second, the adaptive gradient (AdaGrad) method is used to implement the GML estimation for multitarget echo delays and Doppler shifts. Finally, to address the nonlinear estimation problem of interest, the iterative least squares method is used to estimate the multiple target positions and velocities as well as radar system deviations based on the estimated delays and Doppler shifts. Prior information, such as target positions or the probability distribution of the echo coefficient, is not needed in the proposed method. The parameter identifiability is also derived in this article. Numerical simulations show that compared to a method without the estimation of system deviations, the proposed method is more efficient with respect to the derived Cramér–Rao bound.

In the Global Navigation Satellite System-based Synthetic Aperture Radar Interferometry (GNSS-based InSAR) system, the 3-D deformation retrieval accuracy and monitoring efficiency are very poor for the majority of the one repeat-pass period. It is necessary to select the data acquisition time and corresponding satellite combination to obtain better monitoring accuracy and efficiency. In this article, an experimental design algorithm for GNSS-based InSAR is proposed to achieve the highest monitoring efficiency with the restriction of the 3-D deformation retrieval accuracy. First, the joint optimization model is proposed, and based on an analysis of different satellite trajectories, it is proven that the optimal solution can be obtained. Second, the 3-D deformation retrieval accuracy model is derived based on the bistatic configuration, and the monitoring efficiency is evaluated based on the number of effective independent points of a single SAR image. The raw data are employed to indicate the effectiveness of the proposed joint accuracy-efficiency optimization algorithm in GNSS-based InSAR.

With the continuous development of synthetic aperture radar (SAR) systems, multipolarization information has been increasingly applied to numerous fields, and automatic target recognition (ATR) in polarimetric SAR (POLSAR) has been recognized as vital problem. The SAR recognition methods can primarily fall into handcrafted feature-based algorithms and deep learning algorithms. The former exhibits excellent interpretability but insufficient generalization; the latter achieves stronger representational ability but relies on a considerable number of samples. To solve above problems, a feature fusion framework is proposed in this article based on monogenic signal and complex-valued nonlocal network (CVNLNet) for POLSAR target recognition. The proposed feature fusion framework effectively uses the complementarity of handcrafted features and deep features, while making up for the disadvantages of single feature-based methods. First, a Mono-BOVW model is proposed based on monogenic signal and bag-of-visual-words (BOVW) model to extract handcrafted features, which can more fully mine the information covered in POLSAR data in multiscale space. Moreover, CVNLNet is built for deep feature extraction to use both the amplitude and phase covered in POLSAR data. Next, a kernel discrimination correlation analysis algorithm is proposed to jointly analyze and transform the two features, so as to remove redundant information while retaining effective and discriminative information. Experiments on the MSTAR dataset and the GOTCHA dataset show that the proposed framework has superior performance on single polarimetric and fully polarimetric datasets.