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In this article, 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 reradiate electromagnetic (EM) waves, which converts the wave states between free-space waves and guided waves. The bottom phase shifting structure is designed to switch four phase states with a 90° interval for the reflected guided wave. An $8\times8$ 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 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 3.82–3.85 GHz and right-handed circular polarization (RHCP) conversion in the range 3.68–3.82 GHz are realized by switching all the p-i-n diodes ON and OFF.

Space situational awareness (SSA) is a critical issue of space security. The SSA of 3-D information on flying space targets has been operated by inverse synthetic aperture radar (ISAR) and telescope observations. However, how good it is to fuse these two observations, i.e., microwave and optical, has not been well discussed. In particular, it would be difficult to determine the dynamic attitude and its projections of a spinning target, only based on ISAR. This article presents a co-location fusion of ISAR-optical images to acquire a 3-D reconstruction. Making use of semantic segmentation and parallelogram fitting, the dynamic parameters of the target can be acquired, and their projections can be determined. Instead of adopting a scattering point-based method, this article presents a uniform projection representation by fusing the joint ISAR-optical silhouette-based 3-D reconstruction. This approach enables structure-level voxel reconstruction, even for a spinning target. The simulated datasets of the orthogonal projection geometry of fused space target ISAR-optical images demonstrate that good accuracy and favorable 3-D reconstruction can be achieved.

Synthetic aperture radar (SAR) has become a state-of-the-art technology in many applications without being affected by changes in weather and daylight. Since the detection capability of the single-dimensional SAR is limited, the multidimensional (MD) SAR system, e.g., multibaseline and multipolarization, is used to improve its performance. The design of the MDSAR system should be directly related to the specified applications and a quantitatively analytical theory for bound analysis is required to achieve good efficiency. In this article, a mathematical framework for inversion bounds analysis of MDSAR is proposed. First, based on the attributed scattering center (ASC) model, the Fisher information matrix and its corresponding Cramer–Rao lower bound (CRLB) are used to get the error bound of the estimated parameters. Second, considering the discrete sampling (DS) of the parameters, a probability density function-based conversion is conducted to get the DS CRLB. Finally, the mathematical framework for MD acquisitions is established. The simulation-based experimental results show that the theoretical error bound is consistent with the output of the orthogonal matching pursuit (OMP). The error bound of the parameters obtained by the proposed general mathematical framework can be used to evaluate the performance of inversion algorithms under certain MDSAR configurations.

The article proposes a generalized optimal sensing principle for synthetic aperture radar (SAR) imaging, maximizing the mutual information between the sensed object and the reconstructed image with the optimal SAR measurement matrix. Inspired by Shannon’s capacity theorem, the SAR sensing capacity is derived, which represents the maximum mutual information that can be acquired per unit distance or unit area in 1-D or 2-D scenarios. The SAR sensing capacity serves as a theoretical performance bound, guiding the design of SAR sensing systems and enabling reasonable estimation of systems’ performance. Additionally, this article analyzes the relationship between system parameters and the column correlation of SAR measurement matrices, guiding the design of the SAR system. Furthermore, the optimal sensing principle is applied to the variable-resolution SAR (VR-SAR) imaging system. Theoretical simulations are conducted to verify the feasibility of the optimal sensing principle, and examine the relationship between column correlation and SAR parameters. Additionally, the advantages of VR-SAR based on the optimal sensing principle are compared with those of the conventional strip-map SAR mode.

The ionosphere is a vitally dynamic charged particle region in the Earth’s upper atmosphere, playing a crucial role in applications such as radio communication and satellite navigation. The slant total electron contents (STECs) are an important parameter for characterizing wave propagation, representing the integrated electron density along the ray of radio signals passing through the ionosphere. The accurate prediction of STEC is essential for mitigating the ionospheric impact particularly on Global Navigation Satellite Systems (GNSS). In this work, we propose a high-precision STEC prediction model named deep neural operator network (DeepONet)-STEC, which learns nonlinear operators to predict the 4-D temporal-spatial integrated parameter for the specified satellite-ground station ray path globally. As a demonstration, we validate the performance of the model based on GNSS observation data for global and US Continuously Operating Reference Stations (CORS) regimes under ionospheric quiet and storm conditions. The DeepONet-STEC model results show that the three-day 72 h prediction in quiet periods could achieve high accuracy using observation data by the precise point positioning (PPP) with temporal resolution $30~\rm {s}$ . Under active solar magnetic storm periods, the DeepONet-STEC also demonstrated its robustness and superiority than traditional deep learning methods. This work presents a neural operator regression architecture for predicting the 4-D spatiotemporal ionospheric state for satellite navigation system performance, which may be further extended for various space applications and beyond.

High-resolution radar images convey rich information about targets and their geometric characteristics. It remains a challenge to extract the semantic information of target geometry from the speckle-like radar images. We argue that semantic-centric scattering modeling is crucial in this regard. This article proposes to establish a primitive scatterer dictionary (PSD) following the criteria of atomicity, completeness, extensibility, and pixel-wise independence. By sorting out the geometric structure of the smallest atomic geometric unit in common high-frequency approximation methods, the proposed scatterer dictionary consists of primitive 0-D point, 1-D line, and 2-D surface scatterers, being either straight or curved. A unified bidirectional scattering distribution function (BSDF) is derived to analytically model the multidimensional scattering dependences on target attributes, position parameters, and sensor observation configurations. It is further extended to model most typical double and triple scattering structures. The efficacy and applicability of PSD are demonstrated by comparing it to numerical methods in various cases including the commonly seen attributed scattering center (ASC) models.