The phase information of the echo is essential for inverse synthetic aperture radar (ISAR) imaging. Time-modulated metasurface (TMM) generates time-varying reflection phase that can modulate the phase of echo, thus enabling jamming of ISAR imaging, which has great potential for real applications. In practice, covering the whole target with time-modulated metasurface is difficult to achieve. Consequently, the echo model of a radar target partially covered with a time-modulated metasurface is developed, which extends the model of ISAR jamming with TMM fully covering to a more general and near-realistic case in this letter. Then the echoes of each component of the target are obtained by electromagnetic (EM) simulation, and the time-varying reflection phase of the 1-bit time-modulated metasurface is obtained by experimental testing. Finally, using the above data, we validate that ISAR image of the component covered metasurface can be blurred using random time modulation sequence. Meanwhile, duplicated components can be produced in the ISAR image using periodic time modulation sequence. Experimental results of range profile jamming confirm the validity of the conclusions in this letter. The partial defocusing or duplicating of the radar target in the ISAR image can reduce the recognized possibility and enhance the jamming performance, effectively.

In this paper, a simple metasurface superstrate structure is proposed to reduce mutual coupling of both E-plane and H-plane coupled patch antennas. The proposed metasurface is consisted of periodic ring slots arranged on one side of the substrate. The metasurface can suppress the surface wave and partly change the coupling mode, which can achieve isolation enhancement of more than 40dB at resonant frequency in both E/H plane coupled patch antennas. The edge-to-edge spacing between antenna elements is 2mm ( $0.023 \lambda_0$ ). The prototypes of E-plane and H-plane arrays with metasurfaces are fabricated, and the experimental measurements and the simulation results are in good agreement.

The circularly polarized (CP) antennas with high gains have gained growing interest due to their significant advantages, but the existing works usually suffer from narrow bandwidths. In this work, a novel circularly polarized folded transmitarray antenna (CPFTA) inspired by the characteristic mode analysis (CMA) method is proposed to achieve a broadband and high-gain CP radiation. The proposed CPFTA consists of a top-layer multifunctional metasurface (MTS), a bottom-layer polarization conversion metasurface (PCM) integrated with a linearly polarized (LP) microstrip feed source. Enabled by the CMA, the unit cell etched with an asymmetric U-slot of the MTS is optimized to generate two pairs of CP modes to broaden the bandwidth of the CPFTA. Moreover, a modified broadband E-shaped LP microstrip antenna is integrated with the PCM, resulting in a compact profile. A CPFTA prototype is fabricated and characterized to verify the effectiveness of the design. The measured results show that the fabricated antenna could obtain an impedance bandwidth of 31.7%, a 3 dB axial ratio (AR) bandwidth of 36.5%, and a 3 dB gain bandwidth of 30.1% with a peak gain of 22.35 dBic at 9.55 GHz.

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) has shown significant potential in high-resolution radar imaging. However, the Bessel function modulation (BFM) effect caused by the vortex wave’s physical nature influences the imaging performance, especially under the limited OAM modes. To address this issue, we first establish a computationally efficient 2-D vortex radar imaging model and analyze the influence of the BFM effect on vortex radar imaging from the point spread function (PSF). Then, we propose a novel EM vortex radar imaging algorithm based on joint low-rank and sparse constrained representation. Specifically, the vortex radar echo data exhibits the inherent low-rank property, and hence a low-rank regularization term is introduced to remove the BFM effect. Moreover, a constraint corresponding to the image sparsity is adopted to eliminate the interference caused by the limited OAM modes. Hence, the high-resolution vortex radar imaging problem is modeled as a double-constrained underdetermined optimization problem. An iterative algorithm based on the alternating direction method of multipliers (ADMM) framework is further designed to solve the problem with high efficiency. Experimental results based on synthetic, EM, and measured data validate the effectiveness of the proposed algorithm.

Optical and Synthetic Aperture Radar (SAR) imagery offers a wealth of complementary information on a given target, attributable to the distinct imaging modalities of each component image type. Thus, multimodal remote sensing data have been widely used to improve land cover classification. However, fully integrating optical and SAR image data is not straightforward due to the distinct distributions of their features. To this end, we propose a land cover classification network based on multimodal feature fusion, i.e., MFFnet. We adopt a dual-stream network to extract features from SAR and optical images, where a ResNet network is utilized to extract deep features from optical images and PidiNet is employed to extract edge features from SAR. Simultaneously, the iAFF feature fusion module is used to facilitate data interactions between multimodal data for both low- and high-level features. Additionally, to enhance global feature dependency, the ASPP module is employed to handle the interactions between high-level features. The processed high-level features extracted from the dual-stream encoder are fused with low-level features and inputted into the decoder to restore the dimensional feature maps, generating predicted images. Comprehensive evaluations demonstrate that MFFnet achieves excellent performance in both qualitative and quantitative assessments on the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the OA and Kappa metrics by 7.7% and 11.26% on the WHU-OPT-SAR dataset, respectively.

In hyperspectral imagery, target detection algorithms are usually based on the spectral signature information. Due to the advance of the spatial resolution of hyperspectral sensors, the ground sample distance may be much smaller than the size of targets. As a result, targets often occupy multiple consecutive pixels, which are referred to as multi-pixel targets. In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. Jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.