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 weighted arctangent function is used in conjunction with the spectral method to generate a land–sea junction composite rough surface under the spatially homogeneous and time-stationary hypotheses. The exponential correlation function and the Joint North Sea Wave Project (JONSWAP) spectrum, combined with an experiment-verified shoaling coefficient, are applied to model the land surfaces and the time-varying sea surfaces separately. The second-order small slope approximation (SSA-II) with tapered wave incidence is utilized for evaluating the electromagnetic scattering characteristics and Doppler characteristics of the generated composite rough surface. The influence of land–sea interface factors on radar cross-section (RCS) and Doppler shift of radar echoes is investigated in detail by comparing the RCS and Doppler spectra of the land–sea junction composite rough surfaces with those of finite-depth sea surfaces. It can be found that the Doppler spectra of the land–sea junction composite rough surface is narrower than that of the finite-depth sea surface under upwind directions and wider than that of the finite-depth sea surface under crosswind directions.

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 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 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

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.