Shui-Rong Chai

This paper proposes a hybrid Vector Radiative Transfer Shooting (VRT-S)-Bouncing Ray (BR) method, referred to as the VRT-S-BR method, for predicting composite electromagnetic scattering from targets above vegetation-covered rough surfaces. In this proposed framework, the vegetation layer is modeled as a stratified random medium and incorporated into the BR solver through VRT-S-derived amplitude modulation and deterministic phase compensation. Specifically, an offline database of vegetation-induced complex reflection coefficients is first generated using the VRT-S model over a set of incidence angles. During the BR ray-tracing process, these coefficients are used to replace the conventional Fresnel reflection terms on a per-interaction basis, thereby accounting for vegetation-induced attenuation and coherent scattering effects. In addition, a facet-dependent phase compensation scheme is introduced to describe propagation-path variations of individual rays through the vegetation canopy, avoiding the empirical random phase perturbation used in previous hybrid models. The proposed method is validated against field-measured backscattering data over natural grassland, achieving root mean square height (RMSE) values of 1.82 dB and 3.10 dB for horizontal-horizontal (HH) and vertical-vertical (VV) polarizations, respectively. Numerical results further demonstrate the capability of the method to characterize target–vegetation coupled scattering under different percentages of vegetation cover, vegetation heights, terrain backgrounds, and bistatic observation geometries.

In this paper, a novel coupling effects evaluation and quantification framework based on the scattering center model (SCM), termed CEFSCM, is proposed to efficiently solve electromagnetic (EM) coupling problems for cluster targets. Unlike classical SCM (only characterizing overall scattering fields), the CEFSCM innovatively introduces a formation topology-based joint scattering center model (JSCM). It first represents clustered targets' scattering fields as the superposition of contributions from intrinsic scattering centers (ISCs) and coupled scattering centers (CSCs). Quantitative criteria for coupling effects are provided, and three coupling regions are defined via sensitivity analysis. Deploying JSCM regionally instead of indiscriminate numerical calculation significantly reduced computational redundancy. Numerical validation confirms CEFSCM's effectiveness in evaluating coupling effects for cluster members. Compared with the SBR-PO/PTD method, CEFSCM cuts time consumption by over 99% while limiting errors to within 3 dBsm. Accurate reconstruction of cluster scattering fields is achieved through model reuse, reducing errors by over 48% compared with neglecting coupling effects.

To solve the electromagnetic (EM) scattering problems involving a single, complexly moving perfectly electric conductor (PEC) with traditional adaptive cross approximation - method of Moment (ACA-MoM), the excitation vector and the impedance matrix need to be established repeatedly. Moreover, the matrix equation must be iteratively solved at each point in the movement trajectory. This leads to an increase in computational time, especially for targets exhibiting intricate movements. To address these challenges, a new algorithm named multiple time ACA-MoM (MT-ACA-MoM) is proposed and validated in this letter. This algorithm exploits the translation rotation invariance of the Green's function. Consequently, regardless of the target's rotation and translation, the impedance matrix remains unchanged. Therefore, only one impedance matrix needs to be established and stored, which significantly reduces filling time. For the multiple-time problems, the excitation matrix exhibits high rank deficiency. Therefore, in MT-ACA-MoM, ACA is utilized to compress both the impedance matrix and the excitation matrix. Numerical results demonstrate a reduction in the number of matrix equations from 2000 to approximately 100 to 200, leading to a further decrease in solving time of up to 94% and a total speedup of over 700 times

To solve the electromagnetic (EM) scattering from multiple identical rigid targets with traditional adaptive cross approximation method of moments (ACA-MoM), the multiple targets are treated as a single entity. ACA is introduced directly to the octree’s finest layer neglecting the characteristics of the targets. This leads to redundant storage of submatrices, low simulation efficiency, and large memory requirements. To address these limitations, a new algorithm named Dual-ACA-MoM is proposed and validated in this article. The new approach leverages the translation rotation invariance of the Green’s function, requiring only the calculation and storage of a single self-interaction impedance submatrix. This significantly reduces simulating time and memory requirements. Moreover, ACA is applied to two levels in Dual-ACA-MoM: the octree’s finest layer and the target layers. This dual application further reduces matrix filling, CPU time, and memory requirements. The numerical results demonstrate that the proposed algorithm effectively reduces memory requirements and simulation time while maintaining computational accuracy compared to both MoM and ACA-MoM.

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction.

In this article, the small slope approximation in conjunction with the equivalent medium method is applied to analyze the acoustic scattering from a rough bubbly ocean surface, in which seawater with bubbles is regarded as a special medium, and the effect of subsurface bubbles is considered to have three main aspects: First, the attenuation effect, which is manifested as sound energy absorption, effective wave number correction, and sound speed reduction. Second, the modulation of incident and scattering angles of sound waves. Third, the volume scattering by bubbles. Also, it should be pointed out that different from previous papers, the rough sea surface is established, the acoustic scattering from each rough sea surface is simulated and an ensemble average is performed. By comparing with critical sea test results, it is found that the algorithm in this article can effectively predict the acoustic scattering from the sea surface with bubbles. The bistatic and monostatic acoustic scattering strength of the rough bubbly ocean surface under different conditions are calculated and discussed in detail. The results show that the peak of scattering strength appears in the direction of the specular scattering angle, and the farther away from the specular direction, the smaller the scattering strength. As the wind speed increases, the scattering strength first decreases and then stabilizes in the specular scattering direction, first increases and then stabilizes in the forward scattering direction, and continuously increases in the back-scattering direction.

In this paper, an adaptive TLS-ESPRIT algorithm based on analytical perspective is proposed to efficiently construct the scattering center mode and reconstruct the RCS of a PEC target. Compared with existing researches, the proposed method does not require knowing the number of scattering centers, which means that it can be applied to the construction of SCMs for non-cooperative targets. Compared with ISAR and statistical methods, this work strikes a balance between accuracy and efficiency through optimization of the core technology in the RCS reconstruction process. The effectiveness of the algorithm is verified by comparing the results derived by our method with those from the measurement.

In this paper, a hybrid method combining shooting and bouncing ray (SBR), the modified equivalent current approximation (MECA) and the physical theory of diffraction (PTD) is applied to solve the electromagnetic (EM) scattering problems of targets in land-sea junction area. OpenMesh and OpenGL frameworks and the kd-tree data structure are adopted to improve the simulation efficiency. The reliability of the method is derived from comparing the result with the multilevel fast multipole algorithm (MLFMA) in FEKO software. Utilizing the new method, EM scattering characteristics including the far-field radar cross sections (RCS) and one-dimensional high resolution range profiles (HRRP) of ship targets in land-sea junction area are calculated.

To improve ship safety and increase ship concealment, we introduce a nonconvex regularization with a Cauchy-based penalty for discussing the influence of ship parameters and speckle noise in numerically simulated SAR images. First, the Kelvin wake geometry was modeled based on the classic theory of ship wave generation. Second, the scattering echo of the Kelvin wake was calculated using the two-scale method (TSM). Then, using the range-Doppler algorithm (RDA), the scattering echo data obtained by the TSM were processed to obtain the Kelvin wake in SAR images. Finally, the wake was reconstructed in the Radon domain using the Cauchy proximal splitting based on the forward–backward algorithm. The simulation results showed that Kelvin wakes were more easily detected in HH polarization with a large pitch angle and X-band, based on which the influence of ship parameters and speckle noise on the detection of ship wake in numerically simulated SAR images was discussed at different wind speeds. The research conclusions are of value to the development of ship wake stealth technology and the improvement of ship safety.