Synthetic aperture interferometers formed by satellite formations have been adopted to improve spatial resolution. Due to the limited number of satellites and limited integrated time, the use of sparse baselines can result in distorted reconstructed images, which will generate false targets or miss true targets. When detecting a target on the Earth from a geostationary orbit, the target usually occupies only one pixel, and it is almost submerged by noise. Considering the slow-varying characteristics of the observation area, combined with historical observation data and the motion characteristics of the target itself, a target detection method based on multi-frame snapshot images is proposed. Firstly, the observation background is estimated using multi-frame historical data, and background elimination is used to suppress the background noise. Then, potential targets are selected using the local brightness temperature characteristics of the targets. Lastly, the target motion tracks are applied to erase false targets and correct the positions of missed targets. Simulation experiments have been conducted, and the false alarm rate and the missing alarm rate are counted for randomly distributed targets.

In the past two decades, there has been a tremendous increase in demand for services requiring a high bandwidth, a low latency, and high data rates, such as broadband internet services, video streaming, cloud computing, IoT devices, and mobile data services (5G and beyond). Optical wireless communication (OWC) technology, which is also envisioned for next-generation satellite networks using laser links, offers a promising solution to meet these demands. Establishing a line-of-sight (LOS) link and initiating communication in laser links is a challenging task. This process is managed by the acquisition, pointing, and tracking (APT) system, which must deal with the narrow beam divergence and the presence of satellite platform vibrations. These factors increase acquisition time and decrease acquisition probability. This study presents a framework for evaluating the acquisition time of four different scanning methods: spiral, raster, square spiral, and hexagonal, using a probabilistic approach. A satellite platform vibration model is used, and an algorithm for estimating its power spectral density is applied. Maximum likelihood estimation is employed to estimate key parameters from satellite vibrations to optimize scan parameters, such as the overlap factor and beam divergence. The simulation results show that selecting the scan path, overlap factor, and beam divergence based on an accurate estimation of satellite vibrations can prevent multiple scans of the uncertainty region, improve target satellite detection, and increase acquisition probability, given that the satellite vibration amplitudes are within the constraints imposed by the scan parameters. This study contributes to improving the acquisition process, which can, in turn, enhance the pointing and tracking phases of the APT system in laser links.

DAocheng Radio Telescope (DART) is a radio synthetic aperture array consisting of 313 antennas, dedicated to monitoring solar activities and observing cosmic radio sources. To meet the real-time demands of interferometric brightness temperature imaging, we propose a deep learning–based fast-imaging method (DLSI). This method takes dynamically projected baselines and visibility functions as joint inputs, reconstructing brightness temperature images through a single forward pass. Extensive experiments demonstrate that the DLSI achieves imaging quality on DART solar observation data comparable to traditional methods such as gridding combined with CLEAN while reducing computational time by 3 orders of magnitude. Furthermore, under 15%–40% random antenna failure scenarios, the network maintains robust imaging performance, demonstrating exceptional adaptability to hardware malfunctions and strong generalization capability.

针对抵近探测中平台运动、抖动导致的动态图像序列模糊及分辨率低的问题, 提出了一种基于BasicVSR++的视频超分辨方法. 通过引入空间和通道注意力机制强化细节特征提取, 结合共享投射权重、多组机制和采样点调制优化对齐模块, 弥补正则卷积在长距离依赖与自适应空间聚集的不足. 采用下采样与低通滤波器结合的方式, 减少高频成分, 提升抗图像抖动鲁棒性, 同时引入新上采样模块, 通过融合局部与全局特征生成自适应上采样核, 进一步扩展感受野, 以更好地恢复全局结构并重建细节. 仿真实验结果显示, 本文提出的方法在峰值信噪比(PSNR)和结构相似性(SSIM)指标上, 分别比原始方法提高了2.2%和2.1%, 验证了本文方法在抵近探测图像序列超分辨率重建质量提升方面的有效性.