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.