Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, variations in target orientation, and the challenges posed by non-cooperative targets (i.e., targets without cooperative transponders or external markers), limited viewpoint coverage results in a small-sample problem that severely constrains the application of deep learning to SAR image interpretation and target recognition. To address this issue, this paper proposes a multi-target, multi-view SAR image generation method based on conditional information and StyleGAN2, designed to generate high-quality, angle-controllable SAR images of typical targets from limited samples. The proposed framework consists of an angle encoder, a generator, and a discriminator. The angle encoder employs a sinusoidal encoding scheme that combines sine and cosine functions to address the discontinuity inherent in one-hot angle encoding, thereby enabling precise angle control. Moreover, the integration of SimAM and IAAM attention mechanisms enhances image quality, facilitates accurate angle control, and improves the network’s generalization to untrained angles. Experiments conducted on a self-constructed dataset of typical civilian targets and the SAMPLE subset of the MSTAR dataset demonstrate that the proposed method outperforms existing baselines in terms of structural fidelity and feature distribution consistency. The generated images achieve a minimum FID of 6.541 and a maximum MS-SSIM of 0.907, while target recognition accuracy improves by 6.03% and 7.14%, respectively. These results validate the feasibility and effectiveness of the proposed approach for SAR image generation and target recognition tasks.

To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system.

Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) enhances imaging resolution by extending the virtual aperture without increasing the physical size of the antenna. However, its multichannel architecture inevitably introduces amplitude and phase inconsistencies, which significantly degrade image quality. Existing calibration methods typically rely on high-SNR reference targets, but the bistatic geometry inherent to MIMO-SAR systems, combined with the non-ideal scattering characteristics of reference targets, introduces significant estimation bias in channel amplitude and phase calibration. To address this issue, a minimum deployment distance threshold for trihedral corner reflector-based calibration is first determined to mitigate bistatic-induced disturbances. Then, a two-step estimation strategy is proposed to effectively suppress residual errors and improve the accuracy of calibration. Specifically, the initial estimation of amplitude and phase errors is obtained using the Projection Approximation Subspace Tracking algorithm. Subsequently, fine correction is achieved by minimizing image entropy, employing a joint optimization scheme based on Adamax and the Conjugate Gradient method, which enhances convergence stability and avoids local minima. Simulation and experimental results confirm the effectiveness of the proposed method in accurately estimating channel mismatches and substantially enhancing imaging quality. Furthermore, measured data demonstrate its robustness and applicability in high-precision imaging under complex observation conditions.