In mirrored aperture synthesis (MAS), the existing brightness temperature image reconstruction methods include inverse cosine transform and impulse matrix reconstruction methods. However, the quality of the MAS brightness temperature images reconstructed by the existing methods is still poor and needs to be improved. This article proposes a method of MAS brightness temperature image reconstruction with deep convolutional neural network (CNN). The network includes two fully connected (FC) layers, multiple convolutional layers, and deconvolutional layers, which realize the image reconstruction for MAS. This method uses deep CNN to learn the MAS image reconstruction mapping and system errors, so as to improve the performance of the brightness temperature image reconstruction. Both simulation and experimental results verify that the performance of the proposed MAS-CNN method is better than the existing MAS image reconstruction methods.

Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed.

A phase cancellation-assisted binary gray wolf optimization (BGWO) algorithm is proposed in this article to create an advanced design methodology for ultrawideband radar cross-section (RCS) reduction metasurface. By embedding phase-difference constraints into the initialization stage and incorporating fitness-guided refinement, the method enables rapid generation of AMC configurations with enhanced efficiency, accuracy, and adaptability to diverse metasurface architectures. Characteristic mode theory (CMT) is employed to validate the phase cancellation mechanism, enabling quantitative validation of current cancellation mechanisms and revealing the modal contributions that drive broadband RCS reduction. As a demonstration, a triangular checkerboard (TCB) metasurface is designed, enabling broader angular diffusion and effective multidirectional RCS reduction. With a unit cell periodicity of $0.176\lambda _{L}$ and a thickness of $0.08\lambda _{L}$ , the design satisfies the electrically small condition, favoring angularly stable and low-profile implementation. It features a 10 dB RCS reduction over a 94% fractional bandwidth (4.4–12.2GHz) and exhibits polarization insensitivity and maintains angular stability up to ${40}^{\text {o}}$ for both linear polarization (LP) and circular polarization (CP). Simulation and experimental results confirm the efficacy of the proposed method, underscoring its potential for advancing stealth material development.

When observing the Earth from space, the synthetic aperture radiometer antenna array is sometimes set as a non-uniform array. In non-uniform synthetic aperture radiometer image reconstruction, the existing brightness temperature image reconstruction methods include the grid method and array factor forming (AFF) method. However, when using traditional methods for imaging, errors are usually introduced or some prior information is required. In this article, we propose a new IASR imaging method with deep convolution neural network (CNN). The frequency domain information is extracted through multiple convolutional layers, global pooling layers, and fully connected layers to achieve non-uniform synthetic aperture radiometer imaging. Through extensive numerical experiments, we demonstrate the performance of the proposed imaging method. Compared to traditional imaging methods such as the grid method and AFF method, the proposed method has advantages in image quality, computational efficiency, and noise suppression.

In passive microwave remote sensing of the Earth, compared with real aperture radiometer, synthetic aperture radiometer (ASR) is a very powerful instrument with many advantages. However, the system design is more complex than the real aperture radiometer, and the hardware ideality is often not guaranteed. The nonideal characteristics of the system hardware will bring a variety of errors to the system, which will cause the Fourier transform relationship between the visibility function and the brightness temperature image to no longer be established, thus reducing the quality of microwave brightness temperature image reconstruction by traditional methods. In this article, a new microwave brightness temperature image reconstruction method for ASR by transformer is proposed. This method uses a specially designed transformer structure to extract the spectrum features in the visibility function. This method learns the mapping relationship between the visibility function and the original scene brightness temperature image through the supervised learning method, and learns as much as possible the spectrum information contained in the original scene brightness temperature image. Moreover, when there are missing baselines, this method will supplement the missing observation frequency information, so as to obtain better reconstructed image quality. With the above-mentioned advantages, this method can suppress the Gibbs oscillation, and greatly reduce the sidelobe. Compared with the existing reconstruction methods, whether missing baselines or not, the proposed image reconstruction method by transformer has advantages in image quality. We verify the performance of this brightness temperature image reconstruction method through simulation and experiment.