Inverse synthetic aperture radar (ISAR) imaging can provide high resolution images of targets. More recently, the unfolding algorithm has successfully realized fast and efficient ISAR imaging by providing a systematic connection between the traditional iterative algorithms widely used in ISAR imaging and data-based deep learning. However, once the unfolding framework is trained, the layer-dependent parameters are fixed and difficult to adapt to the variations in test scenarios, and usually, retraining is required. This work proposes an ISAR imaging framework based on hypernetwork, which can dynamically generate the unfolding network's internal parameters to accommodate the various scenarios. Specifically, the basis of the framework is an unfolding network based on the generalized expectation consistent approximation (GEC) phase recovery algorithm, where the damping factor is employed for data-driven learning. Instead of directly learning a set of optimal damping factors, the key is to develop a hypernetwork that trains an intelligent controller as the main unfolding network that can dynamically generate the damping factors according to testing scenarios. Thus, it exhibits strong robustness to various scenarios. Simulation and measurement experiments show that the proposed method exhibits excellent performance, robustness and focusing accuracy.

Electromagnetic (EM) vortex waves carrying orbital angular momentum (OAM) have a helical wavefront structure, which allows the echo signal to contain the azimuthal information of the target when performing radar detection tasks, thus they show great potential in the field of staring imaging. It has been demonstrated that EM vortex radar (EMVR) based on the uniform concentric circular arrays (UCCAs) achieves equal divergence angles, so that the target can be simultaneously illuminated by multimode OAM beams. However, the imaging performance of EMVR is affected by the Bessel function term, which makes it difficult to achieve focused imaging results under limited OAM modes and apertures. To address this issue, a fast and high-resolution method based on accelerated 2-D alternating direction method of multipliers (2-D-ADMM) is proposed. First, an imaging model based on beam steering is established to effectively improve the detection performance, and the elevation resolution and azimuth resolution for EMVR are derived through the calculation of correlation function. Then, we propose a novel method based on the nonuniform fast Fourier transform (NUFFT) to speed up the matrix-vector multiplication by means of the fast Gaussian gridding NUFFT method. Afterward, EMVR imaging problem is transformed to a target estimation problem, and hence, the proposed 2-D-ADMM is directly applied to the EMVR model in matrix form with high reconstructing efficiency. Numerical simulation and EM calculation simulation have demonstrated the effectiveness of the proposed method.

In radar system, the maneuvering targets undergoing significant angular motion pose a challenge for motion compensation, thus an inverse synthetic aperture radar (ISAR) imaging framework has been proposed to obtain focused ISAR images under sparse aperture conditions. Due to the non-stationary Doppler frequency induced by the targets' complex motion, conventional range Doppler (RD) algorithm suffers from performance degradation. Therefore, we propose a suitable coherent processing interval (CPI) extraction method to find out the relatively stable Doppler range. Since the fact that the extracted CPI typically contains too limited pulses to generate high-quality ISAR images, a block-sparse signal recovery method based on pattern-coupled sparse Bayesian learning strategy for matrix block (PC-MBSBL) is further developed, which introduces the improved pattern-coupled hierarchical Gaussian model as the sparse prior to reconstruct the scattering coefficient. Specifically, the sparsity of each coefficient is represented by the hyperparameters corresponding to its eight adjacent coefficients, rather than solely by its own hyperparameter. The proposed prior model provides flexibility in modeling any block-sparse signals, and facilitates the generation of block structures. The experimental results using simulated data and measured data demonstrate the validity and superiority of the proposed method.

The performance of traditional SAR imaging methods is limited by the system bandwidth, and the imaging results are easily affected by the target motion state. In the process of SAR imaging, the relative motion between the radar and the target will cause the nonlinear phase error of the echo signal, which makes the imaging quality worse. To solve this problem, we propose a high-resolution moving targets SAR imaging method based on Omega-k and Fourier Ptychographic Microscopy (FPM) phase retrieval technology, which realizes phase retrieval by alternating iteration between time domain and 2-D frequency domain. The proposed method is mainly divided into two steps. Firstly, reference function multiply and Stolt mapping are performed to realize echo preprocessing in the 2-D frequency domain, and then the phase error correction of the compensated echo spectrum is completed by FPM method. Several groups of simulation data and measured data are used for experimental analysis, and the results verify the effectiveness of the proposed method.