Deceptive jamming against synthetic aperture radar (SAR) is significant in defending against hostile reconnaissance and securing the region. Traditional jamming approaches primarily aim at single-imagery SAR, signal waveform type, multichannel, array, and degree of freedom. Since the video SAR (VideoSAR) system can enhance reconnaissance capability in detection, recognition, and perception in dynamic region of interest (DROI), it is imperative to devote to the relevant jamming discipline. To the best of our knowledge, it is the first time that a novel deceptive jamming perspective against VideoSAR system is proposed with simultaneously single-channel, single-band, and single-pass configurations. Frame-dependent principle of deceptive modulation against VideoSAR is derived from the video polar format algorithm (PFA). To obtain the VideoSAR deceptive jamming templates with diverse scattering features and high fidelity, a nonsubsampled Shearlet transform scattering characterization controlling approach is proposed for depicting the multidimensional intrinsic correlations of electromagnetic (EM) scattering behaviors. Three high-resolution airborne VideoSAR datasets are employed to confirm the effectiveness of the proposed deceptive jamming in anisotropy scenarios.

In this paper, a temporal mesh-free analytical solution by using the first-order small-slope approximation (SSA-1) method (TSSA-MF) is firstly proposed to analyze the scattering characteristics of the dynamic nonlinear sea surface. The choppy wave model (CWM) is employed to perform the 2-D nonlinear waves. The algorithm starts with incorporating the expression of the nonlinear sea surface fluctuation into the scattering amplitude of the traditional SSA. Then, the TSSA-MF is established to characterize the dynamic nonlinear sea surface by averaging the random time-dependent non-Gaussian surface. The normalized radar scattering cross section (NRCS) can be predicted quickly and accurately by the proposed TSSA-MF. And, comparing with the scattering amplitude of traditional SSA in the statistical sense, the proposed TSSA-MF includes the time variable. Therefore, the time auto-correlation function (TACF) and the Doppler spectrum of nonlinear and linear sea surfaces are analyzed. The simulation results show that the double-peak phenomenon of the CWM is not obvious in the crosswind compared to linear sea surfaces. It should be noted that, mesh generation and averaging a large number of samples can be avoided for the proposed TSSA-MF, so that the calculation time is substantially reduced with encouraging accuracy.

Scattering properties of lunar surface rocks are important guides for remote sensing observation of the lunar surface and discrimination of geologic features. Previous studies primarily focused on the scattering effects of surface or partially buried rocks in terms of angle or frequency, with limited research addressing SAR simulation images of rocks on the lunar surface. This study proposes a three-dimensional (3D) scattering model that integrates the vector radiative transfer (VRT) method with lunar surface rock scattering mechanisms while effectively combining topographic data, rock abundance data, and radar parameters. To implement this model, an end-to-end computational framework is developed to process input datasets and apply the 3D scattering model for backscattering coefficient simulation, reducing uncertainties in the input parameters. Several case studies are performed on Mini-RF observational data. Simulation results demonstrate that the proposed approach shows good consistency with radar data, particularly in terms of distribution characteristics and average backscattering coefficient errors, with an error margin of less than 3 dB.

In this work, we present a novel perspective on detecting out-of-distribution (OOD) samples and propose an algorithm for sample-aware model selection to enhance the effectiveness of OOD detection. Our algorithm determines, for each test input, which pre-trained models in the model zoo are capable of identifying the test input as an OOD sample. If no such models exist in the model zoo, the test input is classified as an in-distribution (ID) sample. We the-oretically demonstrate that our method maintains the true positive rate of ID samples and accurately identifies OOD samples with high probability when there are a sufficient number of diverse pre-trained models in the model zoo. Extensive experiments were conducted to validate our method, demonstrating that it leverages the complementarity among single-model detectors to consistently improve the effective-ness of OOD sample identification. Compared to baseline methods, our approach improved the relative performance by 65.40% and 37.25% on the CIFAR10 and ImageNet benchmarks, respectively.

The synthetic aperture radar (SAR) imaging environment, especially with limited samples, poses a serious challenge to automatic target recognition (ATR) in modern electronic reconnaissance systems. To enhance SAR image recognition performance, this study proposes a technique leveraging a diffusion model and scattering information. This method involves SAR image generation and scattering information processing to improve generalization with few-shot samples. First, the number of few-shot SAR samples was augmented using the denoising diffusion probabilistic model (DDPM). Then, the scattering information is extracted to stably calculate SAR image similarity from the scattering mechanism. Finally, the recognition task is effectively accomplished through the optimal integration of the recognition network and scattering similarity. Simulation results demonstrate that the proposed method achieves superior SAR image generation quality and recognition accuracy compared to the existing methods when the available data are extremely limited.

An efficient mixed-potential integral equation formulation is proposed for the analysis of two-dimensional (2-D) periodic dielectric objects above the half-space. The spatial domain half-space Green’s functions are obtained from the corresponding spectral domain Green’s functions via the discrete complex image method (DCIM) combined with the matrix pencil method (MP) technique. Then, the Poisson’s summation formula is used to express the periodic Green’s function via a combined summation of spectral and spatial terms. However, the half-space Green’s function with 2-D periodicity is commonly expressed as spatial and spectral infinite series that leads to bad convergence. In order to effectively calculate the components of dyadic and scalar mixed-potential half-space periodic Green’s functions, a novel acceleration method is proposed. In this work, the modified Ewald method is applied to the spatial series to accelerate periodic Green’s functions convergence. Numerical results are provided to validate the efficiency of the proposed method. Compared with the traditional spatial method, the accelerated algorithm has faster convergent speed.

Machine learning-based automatic target detection in video synthetic aperture radar (VideoSAR) has great potential for raising the reconnaissance capability in dynamic region of interest (DROI). In this article, a novel systematic perspective, called low-rank plus sparse multiple-term decomposition (LRSMTD) for simultaneously single-channel, single-band, and single-pass (SCSBSP) VideoSAR configuration is proposed to track the ground defocusing targets. To address the target features of circular VideoSAR imaging, we extend the polar format algorithm (PFA) via exploiting a priori knowledge. In accordance with both the revealed imaging and imagery characteristics, we solve the systematic LRSMTD with proximal exchange-based alternating directions method of multipliers (PEADMM), which makes the process interpretable for defocusing target detection. Comprehensive circular SCSBSP airborne VideoSAR experiments reveal the superior detection performance of the systematic LRSMTD and its several subalgorithms with PEADMM, outperforming 11 state-of-the-art algorithms.

A fast and accurate numerical method based on the theory of characteristic mode (TCM) is proposed for analyzing electromagnetic scattering from repetitive multiscale array structures. In order to overcome the low frequency breakdown problem when interacting at short distances between observation and source basis functions, the CMs are extracted from the impedance matrix generated by the augmented electric field integral equation (AEFIE), which is discretized by the method of moments (MoM). Then the CMs of a single unit are used as the global basis functions to reduce the unknowns of the array structures. In addition, the broadband multilevel fast multipole algorithm (MLFMA) based on approximate diagonalization of the Green’s function is implemented to accelerate the matrix-vector multiplications between the impedance matrix and CM vectors. Several numerical examples are given to demonstrate the high efficiency and accuracy of the proposed scheme.