Traditional forward-looking imaging methods of real aperture radar yield unsatisfactory performance in the presence of outliers. In this article, a method based on variational Bayesian (VB) is proposed to obtain forward-looking imaging in the presence of outliers. First, considering the non-Gaussian property of the imaging noise due to the outliers, we propose to use the Student- $t$ distribution to model noise. In this model, the echo signal does not need preprocessing for the outliers. Second, the Laplace hierarchical distribution is introduced to describe the sparsity of the target. Then, the forward-looking imaging problem converts to the optimal problem. Finally, we give the VB derivation to solve the imaging parameter. To illustrate the imaging performance in the presence of outliers, the outliers are randomly added to some angles and the whole scene of the echo signal in the simulations, respectively. From the simulation results, we can see that the proposed method achieves excellent performance for forward-looking imaging in the presence of outliers.

In airborne forward-looking imaging, the azimuth resolution and the imaging efficiency are important. In this paper, we propose a low-dimension sparse Bayesian learning with Doppler compensation (LDSBL-DC) method to improve the azimuth resolution with a low computational complexity in airborne forward-looking imaging. First, since the variant pitching angle causes the space-variant of the Doppler centroid, the Doppler convolution matrix needs to be constructed in each range cell. We construct a Doppler compensation matrix to eliminate the space-variant of the Doppler centroid. After the Doppler centroid compensation, the Doppler convolution matrix only needs to be constructed once. Second, we propose a low-dimensional projection model based on the singular value decomposition. In the low-dimensional projection model, the high-dimension echo data is compressed to low-dimension data. Finally, combining Doppler centroid compensation and low-dimensional projection model, a new forward-looking imaging model is created, and we introduce sparse Bayesian learning (SBL) to estimate the imaging parameters. In the estimation of the targets’ scattering coefficient, we reduce the computational complexity by the matrix transformation. Several simulations are designed to evaluate the performance of the efficient forward-looking imaging method. The simulation results show the LDSBL-DC method can improve the azimuth resolution with a low computational complexity.

Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target.

The number of Printed Circuit Board (PCB) layers is continually increasing with the increase in data transmission rates, and the Signal Integrity (SI) of high-speed digital systems cannot be ignored. Introducing Vertical Interconnect Accesses (VIAs) in PCBs can realize the electrical connection between the top layer and the inner layers, however, VIAs represent one of the most important reasons for discontinuity between the PCBs and package. In this paper, a new optimization scheme for a differential VIA stub is proposed, with 3D full-wave numerical simulation used for modeling and simulation. Results show that this scheme optimizes the return loss and insertion loss while making the signal eye diagram more ideal, which can improve the transmission effect of high-speed signals.

Iterative adaptive approach (IAA) has been applied to scanning radar imaging to improve the azimuth resolution. However, IAA method suffers from limited resolution improvement in low signal to noise ratio (SNR). In this paper, a weighted IAA method is proposed to enhance the azimuth resolution in low SNR by adjusting the weight of scattering point power. Simulation result illustrates the superior performance of the proposed method.