The focusing of bistatic synthetic aperture radar (BiSAR) data is more challenging than the traditional monostatic counterparts because of the strong range-azimuth coupling of echo signal, and the range cell migration (RCM) and Doppler frequency modulation (FM) rate, which are caused by complex geometric configuration. Although several monostatic algorithms have been modified to handle general bistatic cases, these algorithms are derived from the assumption that the flying platforms are moving along a linear trajectory with uniform velocity. In practical situation, the flight path of the spaceborne SAR platform inevitably deviates from the ideal trajectory in the long integration time. In this case, besides the influence of the inherent geometric configuration of BiSAR, the curved trajectory of the platforms also causes an additional range-azimuth coupling and the spatial variance of RCM and Doppler FM rate, which cannot be processed by the traditional algorithms. In this article, considering the curved orbit of the SAR platforms and the motion of ground targets caused by the Earth's rotation, a high-order motion range model is proposed. Based on the range model, the spatial variance characteristic of the BiSAR with curved trajectory is analyzed. Then, an extended azimuth nonlinear chirp scaling (EANLCS) algorithm with an addition of highly varying residual Doppler centroid correction for BiSAR with curved trajectory is proposed. Simulation results show the effectiveness of the range model and the modified algorithm.

In practice, the configuration of spaceborne multistatic synthetic aperture radar (MSAR) is generally three‐dimensional (3D) stereo geometry, under which condition most current algorithms of motion parameter estimation are invalid. In this study, a joint space‐time–frequency method is presented to estimate motion parameters of ground moving target for MSAR of 3D geometry. First, the array expression for azimuth echo named extended space‐time model (ESTM) is derived; then on the basis of ESTM, a spatial time–frequency distribution method based on fractional Fourier transform algorithm is derived; finally the performance of the presented method is analysed, and the advantages comparing with velocity synthetic aperture radar are discussed via some computer simulations. The work provides a feasible method of multi‐channel processing for the 3D MSAR.

This paper introduces an innovative approach for resolvable group target tracking (RGTT) through the integration of classical Bayesian filtering and sophisticated deep learning techniques. Numerous extant RGTT methods oversimplify target motion models and interactions within groups, limiting their applicability and compromising tracking performance. Deep learning methodologies are recurrently leveraged to overcome these challenges owing to their adeptness in delineating intricate mappings. We develop a neural network tailored for RGTT, incorporating a neural ordinary differential equation (Neural ODE) to articulate the motion model of targets. Additionally, it predominantly integrates recurrent neural network (RNN) and graph neural network (GNN) modules. Similar to multi-target tracking tasks, the RNN module is enlisted here to model nonlinear dynamic systems. The distinction lies in the domain of resolvable group targets, where an additional reliance on graph theory becomes imperative for estimating group structures. Consequently, this paper introduces a GNN module into the deep learning network to learn the interaction relationships among targets within the same group. Furthermore, the seamless fusion of this deep network with the labeled multi-Bernoulli (LMB) filter augments the pragmatic viability of the deep learning algorithm Simulation results elucidate the efficacy of the proposed algorithm.

Multi-channel, DBF-based high-resolution wide swath SAR (HRWS-SAR) is dedicated to acquiring large observation width and high resolution in a single pass. However, the existing HRWS-SAR imaging modes are difficult to cover effectively for the coastline area not parallel to the satellite orbit. Combined with the beam direction changing, variable PRI and distance-oriented DBF techniques, the wide-swath range sweep SAR (WRS-SAR) can image the wide coastline area not parallel to the satellite orbit at high resolution in a single-pass. In this study, an extended wide nonlinear chirp scaling (E-WLNCS) algorithm is proposed, which mainly consists of: a time-domain blind range sample reconstruction algorithm combined with phase variation estimation, a non-uniform resampling algorithm combined with Doppler variation correction, and the effectiveness of the proposed modified E-WLNCS algorithm is demonstrated through point target and surface target simulations.

Sea surface waves provide critical information about wind patterns, ocean currents, and underwater topography. Geosynchronous Orbit Synthetic Aperture Radar (GEO SAR) is particularly well-suited for large-scale, continuous monitoring of sea surface waves due to its wide imaging coverage and persistent observation capabilities. Nevertheless, prolonged synthetic aperture durations in GEO SAR systems amplify decorrelation artifacts induced by the spatially heterogeneous and temporally evolving nature of wave motions, posing significant challenges for accurate imaging. Existing GEO SAR moving target imaging techniques rely on analyzing time-frequency signatures to distinguish moving objects from the background. These approaches work well for high-contrast targets such as vessels but are less effective for sea surface waves, where motion is distributed across the entire scene. To address this challenge, this study presents the Geosynchronous Orbit Spatial Correlation SAR (GEOSC-SAR) framework, a novel imaging framework that integrates Model-Based Optimized 2D Spatial Correlation (MBO2D-SC). Three key innovations drive our methodology: (1) A blockwise first-order two-dimensional (BFOTD) kinematic model that parameterizes space-variant wave velocity and acceleration fields; (2) The MBO2D-SC algorithm that dynamically computes phase correction co-efficients through adaptive spatial correlation processing; (3) A closed-loop optimization architecture combining genetic algorithm-driven parameter estimation with a custom wave imaging quality evaluation factor. The framework establishes a self-consistent correction mechanism where motion parameter refinement enhances correlation restoration, while phase compensation reduces residual errors.We analyzed GEOSC-SAR from multiple aspects, including a sensitivity analysis to precisely assess the influence of wind speed and wave amplitude on imaging, imaging performance under superimposed flow fields, the exploration of applications for high-wavenumber truncation mitigation, and computational efficiency analysis. Numerical simulations demonstrate that GEOSC-SAR achieves sub-13% relative error (8.3% in image contrast, 7.6% in grayscale modulation, 12.1% in grayscale modulation balance) compared to original wave surfaces, outperforming existing methods. It demonstrates robustness across conditions (2-12 m/s wind speeds, 0.5-4.5 m wave amplitudes), with superior imaging at high wave amplitudes and low wind speeds, effectively handles superimposed flow fields, and accurately reconstructs wave spectrum in low wavenumber regions while improving spectrum reconstruction in high wavenumber regions versus existing methods. Despite increased computational complexity, efficiency enhancements via leveraging temporal patterns in sea surface motion are underway for large-scale, real-time sea surface monitoring.

Multi‐dimensional reconstruction of ground is one of the most important applications of differential synthetic aperture radar tomography (D‐TomoSAR) system. The ground deformation model is considered to be linear in classical D‐TomoSAR system model. However, the real deformation in practice is deviated from the linear model, and the deformation parameters cannot be estimated accurately by spectral estimation. Here, a polynomial deformation model is proposed to approach the real motion of the ground. Since the deformation history appears as a phase term in the model of D‐TomoSAR system, the corresponding model could be extended to a two‐dimensional (2D) polynomial phase signal (PPS) by introducing the polynomial deformation model. In addition, an effective method combining the 2D product high‐order ambiguity function with 2D‐RELAXation (RELAX) algorithm is provided to estimate the coefficients of this 2D‐PPS. Theoretical analyses and experimental results show that the ground elevation, linear and non‐linear motions can be both estimated accurately, and it is possible to separate scatterers distributed along the slant height within a resolution cell with the proposed method.