Achieving low-power and polarization-insensitive communication is a crucial and challenging task in the Internet of Everything. In this communication, we propose and demonstrate the implementation of two amplitude-reconfigurable metasurfaces, loaded with PIN diodes and operated around 5.8 GHz, to develop a novel polarization-insensitive backscatter communication system. Both communication transmitters using uniform and gradient metasurfaces are able to achieve high-efficiency polarization conversion, and thus can cooperate with the receiver end to mitigate the interference generated by environmental carrier sources. To achieve directional information transmission, we design a corner reflector metasurface and a gradient metasurface, which respectively enhances the gain in the backward and fixed scattering directions. Due to advantages of polarization insensitivity and low energy consumption, this system promises great potential for future engineering applications in the Internet of Things, particularly for smart home, wearable sensing and other related areas.

The orthogonal frequency division multiplexing (OFDM) chirp signal has its special merits of high-range resolution by exploiting a full bandwidth as multiple input multiple output (MIMO) radar signal. After simulation, there are some high peaks in the autocorrelation function and cross correlation function of conventional OFDM chirp signals, which may increase the correlation interference and reduce the detection performance of MIMO radar signal. This letter proposes to reduce the autocorrelation sidelobe peak (ASP) of OFDM chirp signal by subsection imitating the nonlinear frequency modulation (NLFM) signal's time-frequency structure. Since a suitable difference should be made between the subchirp rates and subchirp carrier frequencies of each of the two designed OFDM chirp signals for reducing the cross correlation peaks (CP), the imitated NLFM waveforms' design problem is solved by the proposed optimized method. At last, subchirps of the designed signals are coded by a group of optimized codes. The simulation shows the designed OFDM chirp signals have lower ASP and CP than the conventional ones and referenced previous OFDM signals, and their ASP decreases as the number of subchirps increases.

The study on the scattering mechanism of breaking ship waves provides important theoretical basis to the ship detection and radar imaging in a marine environment. This paper presents a new procedure for evaluating the electromagnetic scattering field of breaking ship waves over a perfect electric conductor sea surface. In this paper, different forms of the near-field ship waves are obtained by the computational fluid dynamics (CFD) method, which provides a new means of analyzing a fully coupled fluid-structure interaction system in a detailed manner. Sufficient grids are used in the CFD numerical simulation to obtain the flow field information of the breaking waves. The accuracy of the breaking wave geometry has been improved obviously, which will lead to a better understanding of the electromagnetic scattering characteristics of breaking ship waves. Considering the multiple scattering between the ship hull and the breaking waves, the scattering field of the sea surface and the ship hull are evaluated by the forward-backward iterated physical optics algorithm. Both scattering characteristics of the breaking ship waves and distribution of the scattering coefficients are obtained. By comparing the bistatic scattering field in different situations, the numerical results show some important effects of the breaking ship waves, which are quite different from the effects of ship models. What is more, the time varying radar cross section of the breaking ship waves are presented based on the dynamic ship waves.

Verifying the neighborhood consensus to remove false correspondence is a popular idea in feature matching. However, traditional neighborhood consensus only considers spatial neighborhoods (SNs), which is not robust in challenging remote sensing tasks. This article extends the traditional neighborhood consensus for improving robustness to the two key issues—significant geometric transformation and repetitive patterns. First, we introduce a novel matching neighborhood (MN) that extends the one-to-one correspondence in traditional neighborhood consensus to one-to-multiple structure to address the repetitive patterns, where one-to-multiple means that multiple matching candidates are preserved in calculating descriptor similarity. Second, the traditional SN is also extended using affine correspondence, which can adaptively address the significant geometric transformations without multiscale processing. On the two bases, we construct a novel extended neighborhood (EN) by combining the extended SN with the MN. Consequently, the false feature correspondences are filtered by measuring the consensus between the ENs. Numerous experiments demonstrate that the proposed method is state-of-the-art (SOTA) in comparison with recent learning and traditional methods, especially for the UAV localization task. We also show that the proposed method is robust to the basic settings, such as the prefiltering threshold and the type of local features.

Ground Penetrating Radar (GPR) is an important non-destructive sensing technology for subsurface detection. However, strong clutter from surface reflection and heterogeneous media often masks weak target responses in B-scan images. Existing low-rank and sparse decomposition (LRSD) methods, such as Robust Principal Component Analysis (RPCA), can separate clutter and targets but suffer from uniform singular value shrinkage and high computational cost; more critically, they operate only at the original resolution, refining only the spatial sampling grid of the migrated image under the inherent physical resolution limit. In this paper, we propose a Joint Super-Resolution and Weighted Low-Rank Sparse Decomposition (JSR-LRSD) framework that synergistically suppresses clutter and improves the spatial detail and target localization of the migrated image without extending its physical resolution. Unlike cascade approaches, which perform clutter removal first and super-resolution afterward and may amplify residual clutter, our method integrates a differentiable super-resolution network directly into the alternating direction method of multipliers (ADMM) iterations. This allows for the sparse target matrix to be regularized by high-resolution image priors, while the high-resolution estimate adaptively adjusts the weighted nuclear norm via total variation feedback. An adaptive weighted nuclear norm is designed using singular values of the observed data and the total variation feedback from the high-resolution target estimate. A randomized singular value decomposition (SVD) is introduced to accelerate computation. Extensive experiments on simulated and laboratory GPR data demonstrate that the proposed JSR-LRSD achieves a higher signal-to-clutter ratio (SCR) compared to conventional and LRSD-based methods, with clear improvement in spatial detail recovery, while maintaining computational efficiency.