Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging technology are powerful tools to acquire high-resolution radar image, which is an important basis for further automatic target recognition (ATR). For ISAR, if the radar frequency is high enough, signals that the radar received usually have strong sparsity when the radar data convert to Fourier domain, and they can be downsampled and restored by compressed sensing (CS). As for the regularization method in CS theory, while L₁ regularization is popular, the Lq(0 < q < 1) regularization is proved to be a sparse regularization framework and could achieve well performance. In this work, inspired by the L₁-based complex approximated message passing (CAMP) method and the L(1/2) regularization framework, we extend the CAMP method into an L(1/2)-based iterative thresholding method in ISAR imaging under the downsampling rate of the scattered fields. The matched filtering (MF) method is widely used to generate the SAR image, where targets are usually overwhelmed by noise and scene background. In order to make the target enhanced to further improve recognition, regularization is adopted in recovering sparse solution and the clutter reduction of MF image. This work implements the L(1/2)-based regularization into the SAR image target enhancement and the scene-noise reduction via CAMP. The SAR image is generated by ideal point scatterers and the real measurement data of RADARSAT-1. Given the sparsity estimated, the experiment results show the advantage of L(1/2) regularization compared with the L₁ regularization.

The physical optics (PO) scattered fields from the electrically large scatterers contain slowly varying amplitude and highly oscillatory phase terms. In this work, firstly, we consider quadratic approximations for the phase term of the PO scattered fields. Secondly, the linear amplitude of the PO scattered fields (L-FPO) is proposed. The simplification of the linear amplitude term is adopted with two reasons. One is that the contributions of the PO scattered fields mainly come from the high-frequency critical points, that is, the stationary phase points, the boundary phase points and the vertex points. When the wave frequency goes large, the linearization of the amplitude term could guarantee the accuracy of the PO scattered fields from the high-frequency critical points. The other reason is that the L-FPO method proposed in this work avoids the time-consuming and complicated calculation of Fresnel integrals. The computational cost of the Fresnel integrals is shrank drastically. For the aircraft model, the CPU time of the proposed method is reduced by one order of magnitude compared with the Gordon’s method, and nearly half compared with the Q-FPO method. In the process of the PO method on determining the lit region of the scatterer, the new shadowing technique pairing a quadratic quadrilateral patch into two sub-triangles achieves high accuracy in determination of the shadow boundary.

In this work, the physical optics (PO) method based on multilayered medium theory is used to analyze high-frequency scattering characteristics of plasma sheath covering an aircraft. The ray intersection method is taken for occlusion judgment, but some errors occur on the edge between illuminated and shadowed regions of a target. Therefore, the iterative edge patch segmentation based on adaptive mesh technique is proposed to reduce this error, which is significant to enhance computational accuracy of the PO method. Furthermore, to simplify the computational process and validate effectiveness of adaptive mesh technique, the plasma sheath covering a RAM-C II aircraft is modeled with a five-layered isotropic medium. Compared with uniform fine segmentation, the proposed adaptive mesh technique markedly reduces the computational cost by an order of magnitude while maintaining accuracy of the PO method. Moreover, high-frequency scattering characteristics of plasma sheath covering a RAM-C II aircraft under different flight conditions are presented via the bistatic radar cross section (RCS) with observable attenuation effects. Hence, the PO method for multilayered media combined with the proposed lit region judgment method is suitable to analyze scattering features of electrically large coated target.

ISAR image data of a single target is sparse in the image domain. Based on this sparseness, we could obtain a high-precision image reconstruction by down sampling the imaging data and getting the sparse solution of the indeterminate equations. In this work, we have studied the sparse data processing theory based on the compressed sensing (CS) method. We focus on the sparse reconstruction of the inverse synthetic aperture radar (ISAR) image. The imaging data is sparsely sampled and restored through the norm regularization framework. We compare the reconstruction results on L ₁ and L _1/2 regularization frameworks, respectively. Then, we concentrate on the relationship between the reconstruction results and parameter settings in the reconstruction framework. Besides, we study the ISAR image in different radar bands. The numerical results show that the L _1/2 regularization framework is better than the L ₁ framework in recovery accuracy and computational efficiency.