Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging technology are powerful tools to acquire high-resolution radar image, which is an important basis for further automatic target recognition (ATR). For ISAR, if the radar frequency is high enough, signals that the radar received usually have strong sparsity when the radar data convert to Fourier domain, and they can be downsampled and restored by compressed sensing (CS). As for the regularization method in CS theory, while L₁ regularization is popular, the Lq(0 < q < 1) regularization is proved to be a sparse regularization framework and could achieve well performance. In this work, inspired by the L₁-based complex approximated message passing (CAMP) method and the L(1/2) regularization framework, we extend the CAMP method into an L(1/2)-based iterative thresholding method in ISAR imaging under the downsampling rate of the scattered fields. The matched filtering (MF) method is widely used to generate the SAR image, where targets are usually overwhelmed by noise and scene background. In order to make the target enhanced to further improve recognition, regularization is adopted in recovering sparse solution and the clutter reduction of MF image. This work implements the L(1/2)-based regularization into the SAR image target enhancement and the scene-noise reduction via CAMP. The SAR image is generated by ideal point scatterers and the real measurement data of RADARSAT-1. Given the sparsity estimated, the experiment results show the advantage of L(1/2) regularization compared with the L₁ regularization.

In this paper, a 2-bit reconfigurable reflective metasurface (RRM) with multifunction for both far-field pattern and polarization performance is proposed. To facilitate the independent design of two orthogonal polarizations as well as the diode integration at the bottom, the radiation and phase shifting parts are designed separately. The top patch is designed to receive and re-radiate EM waves, which converts the wave states between free-space wave and guided wave. The bottom phase shifting structure is designed to switch four phase states with 90° interval for the reflected guided wave. An 8×8 RRM is designed and fabricated using the proposed 2-bit element. Driven by a field programmable gate array (FPGA), the RRM achieves real-time control for different functions of beam deflection, radar cross section (RCS) reduction, and polarization conversion. The measured results show that the steered beam from -30° to +30° can be obtained around 3.83 GHz with different coding matrices, and more than 10 dB RCS reduction from 3.70 GHz to 3.84 GHz can be achieved by properly distributing phase responses randomly. Moreover, a linear polarization (LP) to left-handed circular polarization (LHCP) conversion in the range 3.82 GHz-3.85 GHz and right-handed circular polarization (RHCP) conversion in the range 3.68 GHz-3.82 GHz are realized by switching all the PIN diodes ON and OFF.

To monitor a space target, 3-D reconstruction from a multiview sequence of the inverse synthetic aperture radar (ISAR) imaging is developed. Scattering of a complex electric-large target, e.g., the ENVISAT satellite model, is numerically calculated, and multiview 2-D ISAR imaging can be simulated. Under the sparse sampling ISAR imaging via compressed sensing, the Kanade-Lucas-Tomasi feature tracker is applied to extraction of target feature points. Then, using the orthographic factorization method, 3-D reconstruction of those feature points is produced. A simple hexagonal frustum is first tested for the feasibility analysis. Two sequences of multiview ISAR imaging, one is the ENVISAT model and another real measurements of a space shuttle, are then presented for 3-D reconstruction. Furthermore, a complex multistructure model of the International Space Station is also studied from multiview ISAR imaging under different sparse sampling rates. All results demonstrate good feasibility of the 3-D reconstruction for those target components, e.g., solar panel and antenna.

Deep learning-based synthetic aperture radar (SAR) target recognition often suffers from sparsely distributed training samples and rapid angular variations due to scattering scintillation. Thus, data-driven SAR target recognition is considered a typical few-shot learning (FSL) task. This article first reviews the key issues of FSL and provides a definition of the FSL task. A novel adversarial autoencoder (AAE) is then proposed as an SAR representation and generation network. It consists of a generator network that decodes target knowledge to SAR images and an adversarial discriminator network that not only learns to discriminate “fake” generated images from real ones but also encodes the input SAR image back to target knowledge. The discriminator employs progressively expanding convolution layers and a corresponding layer-by-layer training strategy. It uses two cyclic loss functions to enforce consistency between the inputs and outputs. Moreover, rotated cropping is introduced as a mechanism to address the challenge of representing the target orientation. The moving and stationary Target recognition (MSTAR) 7-target dataset is used to evaluate the AAE’s performance, and the results demonstrate its ability to generate SAR images with aspect angular diversity. Using only 90 training samples with at least 25° of orientation interval, the trained AAE is able to generate the remaining 1748 samples of other orientation angles with an unprecedented level of fidelity. Thus, it can be used for data augmentation in SAR target recognition FSL tasks. Our experimental results show that the AAE could boost the test accuracy by 5.77%.

The Chinese Moderate Resolution Imaging Spectroradiometer (CMODIS) was loaded on the China's SZ‐3 spacecraft. Using an empirical line method, the CMODIS radiance is converted to the water‐leaving reflectance, and is applied to inversion of the suspended sediment concentrations in the Yangtze River estuary. The concentrations ranging between 0 mg/L and 1000 mg/L are well validated by the field measurement data. This study demonstrates an example for the feasibility of the CMODIS data for concentration retrieval of the suspended sediment.