For unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imaging, motion errors cannot be obtained accurately when high precision motion sensors are not equipped on the platform. This means that traditional data-based motion compensation (MOCO) cannot be directly implemented due to large measurement errors. In addition, classic autofocusing techniques, such as phase gradient autofocus (PGA) or map-drift algorithm (MDA), do not perform well with spatially variant errors, greatly affecting the imaging qualities, especially for high-resolution and large-swath cases. In this study, an improved spatially variant MOCO approach based on an MDA is developed to effectively eliminate the spatially variant errors. Based on the coarse and precise MDA chirp rate error estimation, motion errors are optimally acquired by the random sample consensus (RANSAC) iteration. Two-dimensional (2D) mapping is used to decouple the spatially variant residual errors into two linear independent dimensions so that the chirp-z transform (CZT) can be performed for echo data correction. Unlike traditional approaches, the spatially variant components can be compensated without any measured motion information, which indicates that the proposed approach can be applied to the common UAV SAR system with significant measurement errors. Simulations and real data experiments were used to evaluate the performance of the proposed method. The simulation results show that the proposed algorithm is able to effectively minimize spatially variant errors and generate much better imaging results.

The automotive synthetic aperture radar (SAR) can obtain two-dimensional (2-D) high-resolution images and has good robustness compared with the other sensors. Generally, the 2-D high-resolution always conflicts with the real-time requirement in conventional SAR imaging. This article suggests an automotive video SAR (ViSAR) imaging technique based on sub-aperture spectrum fusion to address this issue. Firstly, the scene space variation problem caused by close observation distance in automotive SAR is analyzed. Moreover, the sub-aperture implementation method, frame rate and resolution of automotive ViSAR are also introduced. Then, the improved Range Doppler algorithm (RDA) is used to focus the sub-aperture data. Finally, a sub-aperture stitching strategy is proposed to obtain a high-resolution frame image. Compared with the available ViSAR imaging method, the proposed method is more efficient, performs better, and is more appropriate for automotive ViSAR. The simulation results and actual data of the automotive SAR validate the effectiveness of the proposed method.

Unlike the traditional multi-channel synthetic aperture radar (SAR) three-dimensional (3-D) reconstruction technology, the single-channel SAR with two synthetic apertures in the vertical and horizontal planes, realized by curved trajectory, has advantages of requiring less acquisition data, lower device cost, and fewer limitations on trajectories. However, due to the complex aerodynamic configuration and flight characteristics, critical challenges are faced including the mathematical model establishment, signal property analyses and reconstruction approach design. In this paper, to address these issues, a 3-D vector geometry model of curvilinear SAR (CLSAR) system is presented and its properties, including elevation bandwidth and resolution, are studied in detail. Then, a novel 3-D reconstruction approach for single-channel CLSAR system based on azimuth pre-focusing is presented, in which the cylindrical symmetry of SAR system is utilized to provide accurate azimuth information of targets in observing scene. With the assisted azimuths, the scene imaged in vertical plane can be performed, and the 3-D reconstruction of interested targets can be obtained. Numerical results using simulated data demonstrate the effectiveness of the proposed approach.

Airborne synthetic aperture radar (SAR) is susceptible to atmospheric disturbance and other factors that cause the position offset error of the antenna phase center and motion error. In close-range detection scenarios, the large elevation angle may make it impossible to directly observe areas near the underlying plane, resulting in observation blind spots. In cases where the illumination elevation angle is extremely large, the influence of range variant envelope error and phase modulations becomes more serious, and traditional two-step motion compensation (MOCO) methods may fail to provide accurate imaging. In addition, conventional phase gradient autofocus (PGA) algorithms suffer from reduced performance in scenes with few strong scattering points. To address these practical challenges, we propose an improved phase-weighted estimation PGA algorithm that analyzes the motion error of UAV SAR under a large elevation angle, providing a solution for high-order range variant motion error. Based on this algorithm, we introduce a combined focusing method that applies a threshold value for selection and optimization. Unlike traditional MOCO methods, our proposed method can more accurately compensate for spatially variant motion error in the case of scenes with few strong scattering points, indicating its wider applicability. The effectiveness of our proposed approach is verified by simulation and real data experimental results.

For multirotor unmanned aerial vehicle (UAV) synthetic aperture radar (SAR), the propeller rotation may lead to multi-component high-frequency vibration error (HFVE), seriously affecting imaging quality. To deal with this practical issue, a multi-component HFVE estimation and compensation approach is presented for multirotor UAV SAR, which can be implemented in three steps, i.e., vibration component extraction, vibration parameter estimation, and vibration error suppression. First, an improved ensemble empirical mode decomposition method is utilized to extract the vibration component of HFVE, ensuring the accuracy of component extraction. Then, based on the optimal solution of the correlation function, the parameters of the extracted vibration component are accurately determined to improve the accuracy of the constructed compensation function. Finally, all the vibration components are compensated in cyclic processing to achieve effective suppression of multi-component HFVE. Real data experimental results show that our approach outperforms the existing approach in terms of average impulse response width (0.21 m versus 0.25 m), peak sidelobe ratio (-25.26 dB versus -19.56 dB), and integration sidelobe ratio (-19.43 dB versus -6.54 dB), which verify the effectiveness of our approach.

Due to the large diving trajectory, the design of the imaging algorithm remains an exceptionally difficult task for hypersonic synthetic aperture radar (SAR). In this article, an accurate range model that accounts for the impact of higher-order motion factors on imaging performance is established. Based on the model, the signal characteristics are analyzed, which indicates the design of the imaging approach faces greatly challenges such as severe cross-couplings and significant spatial variations (SV). In view of these issues, a time-frequency hybrid domain imaging algorithm (TFHDIA) is proposed for hypersonic vehicle-borne (HSV) SAR systems with large diving trajectory. The cross-couplings are significantly reduced by azimuth preprocessing. The two-dimensional (2-D) SV in the range cell migration (RCM) and first-order SV in the Doppler parameters (DP) are eliminated through the fractional Fourier transform (FrFT) and the extended keystone transform (EKT). Additionally, the higher-order SV of DP are removed by the phase filter bank, which includes range, azimuth, and cross-coupling spatially variant phase error compensation filters. The proposed algorithm exhibits strong capabilities in eliminating SV and effectively reducing cross-coupling, making it particularly suitable for HSV SAR systems with large diving trajectory. Simulation and actual acquired data results validate the efficacy of the proposed algorithm.