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In this article, an automatic and forward method is realized to establish attributed scattering center models directly from the computer-aided design (CAD) model of the complex target. The main steps include the preprocessing of the CAD model, the separation of scattering sources, the selection of strong scattering sources, and the automatic determination of model parameters. With the proposed method, the scattering sources, scattering mechanisms, and model parameters of the scattering centers can be identified and derived, such that the complicated manual intervention is completely avoided. Moreover, the method establishes the distributed scattering center models formed by curved surfaces with large curvature radii. Therefore, the formation mechanism of the distributed scattering center is extended from typical scattering structures to a general case. Thus, the model of the attributed scattering center is extended and can be applied to describe the scattering from the real structures of the complex target. The geometric shape of the scattering source is distinguished based on the principal curvature radii, which are calculated by differential geometry theory. Thus, the frequency dependence parameter is obtained according to its corresponding relationship with the geometric shape. In addition, based on the automatic method, a technology is studied to diagnose and correct the scattering center models of a target whose CAD model is unknown or partially known. Finally, parametric models of several targets in the Moving and Stationary Target Acquisition Recognition (MSTAR) program are established, and then compared with the measured data. The results validate the effectiveness of the proposed method.

This paper proposes a fast target indexing method based on target geometric models and further conceives a forward method based coarse-to-fine hierarchical synthetic aperture radar (SAR) automatic target recognition (ATR) system. The proposed fast SAR target indexing method is employed for coarse classification before the implementation of fine classification and recognition, resulting in significant acceleration of target recognition. Furthermore, the fast SAR target indexing method is purely based on target geometric models without resorting to any time-consuming electromagnetic (EM) computation, which enables the online and real-time classification. The binary target region and shadow region are selected as the indexing features, and a forward feature prediction method based on optical visibility is developed to predict the features from the target geometric model in real time. Then, the features extracted from the testing SAR image are aligned with the predicted features to establish correlation ship and a fused similarity based on four types of complementary similarity criteria is designed to further implement target indexing. The effectiveness of the proposed fast SAR target indexing method is demonstrated by using the MSTAR dataset.

Fast algorithms for electrically large objects buried in layered media are mainly hindered by two time-consuming processes. One is the table filling of Green's function, and the other is the solving of the impedance matrix equation. For the first, to accelerate the evaluation of the time-consuming Sommerfeld integral in the dyadic Green's function (DGF), the discrete complex image method (DCIM) is introduced to get a closed-form DGF. To further accelerate the calculation of DGF for the volume electric field integral equation (EFIE), DGF is split before applying DCIM. For the second, the iterative solver stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) is combined with DCIM for solving the matrix equations. Meanwhile, the closed-form DGF enables the "spherical-mean" Green's function, which eliminates the singularity of Green's function. Numerical results show that the weaker singularity results in a faster and steadier convergence rate for iterative solvers