In most existing robust array beam pattern synthesis studies, the bounded-sphere model is used to describe the steering vector (SV) uncertainties. In this letter, instead of bounding the norm of SV perturbations as a whole, we explore the amplitude and phase perturbations of each SV element separately, thereby obtaining a tighter SV uncertainty model. On the basis of this model, we formulate the robust pattern synthesis problem from the perspective of the min-max optimization, which aims to minimize the maximum side lobe response, while preserving the main lobe response. However, this problem is difficult due to the infinitely many nonconvex constraints. As a compromise, we employ the worst-case criterion and recast the problem as a convex second-order cone program (SOCP). To solve the SOCP, we further design an alternating direction method of multipliers based algorithm, which is computationally efficient by coming up with closed-form solutions in each step.

Transmit beamforming and artificial noise-based methods have been widely employed to achieve physical-layer (PHY) security. However, these approaches may fail to provide satisfactory secure performance if the channels of legitimate user (LU) and eavesdropper (Eve) are highly correlated, which usually occurs in the case of close-located LU and Eve. The goal of this paper is to address the PHY security problem for proximal LU and Eve in millimeter-wave transmissions. To this end, we propose a novel frequency diverse array (FDA) beamforming approach, which intentionally introduces some frequency offsets across array antennas to decouple the highly correlated channels of LU and Eve. By exploiting this decoupling capability, the FDA beamforming can degrade Eve's reception and thus enhance PHY security. Leveraging FDA beamforming, we aim to maximize the secrecy rate by jointly optimizing the frequency offsets and the transmit beamformer. This secrecy rate maximization problem is difficult due to the tightly coupled variables. However, we show that it can be reformulated into a form only depending on the frequency offsets. Building upon this reformulation, we further employ the block successive upper-bound minimization method to iteratively obtain a solution with stationary convergence guarantee. Numerical results demonstrate that FDA beamforming can provide higher secrecy rate than conventional beamforming, especially for proximal LU and Eve.

A scheme of feature analysis in synthetic aperture radar (SAR) imaging based on compressed sensing (CS) is presented. First, a conventional imaging algorithm, such as range-Doppler algorithm or chirp-scaling algorithm, is used to acquire the complex image of the target scene. Then, a greedy algorithm is applied to this complex image to locate the scattering coefficients of the target, where the research of the peak and the removal of the point spread effect are involved. Third, the point set of each target is determined by a clutter method and the range of the target is further fitted by a geometric shape. They are, finally, used to extract the feature parameter of the target. A series of experimental results using real field data demonstrates the effectiveness of our method.

A novel despeckling algorithm for synthetic aperture radar (SAR) image is presented. This algorithm focuses on despeckling a local region for each target pixel. Thus, the calculations of similarities and weights are located within a limited region rather than the entire image. Also, the similarity is measured by using the Euclidean distance of features between the target pixel and neighborhood pixel. In addition, a cosine integral image technique is applied to this scheme to improve the computation efficiency. The weight function is first decomposed into a linear combination of cosine functions, and each convolution with a cosine function requires a constant number of operations per pixel. Then, all the summation operations are carried out by the summed image method. A series of experiment results demonstrates the effectiveness of our method.