High-precision relative-state measurement technology is one of the key technologies for achieving the precision formation flying of distributed spacecraft. This paper conducts a comprehensive analysis of the precision formation-flying projects of distributed spacecraft in various countries. In the context of practical application, the specific mission configuration, orbit distribution, measurement technology, and payload of the project are summarized. On this basis, the relative state measurement techniques are outlined for the first time, using non-autonomous measurement techniques, autonomous measurement techniques, and new composite relative measurement techniques. A detailed analysis of GNSS (Global Navigation Satellite System)—relative measurement, laser measurement, infrared measurement, RF measurement, visible light visual measurement, and multiple composite measurement methods is conducted. The applicable scenarios of each measurement method are thoroughly discussed from several aspects, such as the technical scheme, system design, accuracy requirements, advantages, and shortcomings. In addition, this paper proposes the concept of adopting a multidisciplinary optimization architecture from the perspective of the overall design of the precision formation of the distributed spacecraft. It enables relative-state measurement payload selection and property indicator optimization, on the premise of optimizing the overall formation performance. Finally, the optimization direction and future development trend of the spacecraft precision formation flight project and relative state measurement technology are established.

In on-orbit services, the relative position and attitude estimation of non-cooperative spacecraft has become the key issues to be solved in many space missions. Because of the lack of prior knowledge about manual marks and the inability to communicate between non-cooperative space targets, the relative position and attitude estimation system poses great challenges in terms of accuracy, intelligence, and power consumptions. To address these issues, this study uses a stereo camera to extract the feature points of a non-cooperative spacecraft. Then, the 3D position of the feature points is calculated according to the camera model to estimate the relationship. The optical flow method is also used to obtain the geometric constraint information between frames. In addition, an extended Kalman filter is used to update the measurement results and obtain more accurate pose optimization results. Moreover, we present a closed-loop simulation system, in which the Unity simulation engine is employed to simulate the relative motion of the spacecraft and binocular vision images, and a JetsonTX2 supercomputer is involved to conduct the proposed autonomous relative navigation algorithm. The simulation results show that our approach can estimate the non-cooperative target’s relative pose accurately.

Ultra-low Earth orbit (LEO) satellites are widely used in the military, remote sensing, scientific research, and other fields. The ultra-LEO satellite faces the harsh aerothermal environment, and the complex variable attitude task requires the radiator of the satellite to not only meet the heat dissipation requirements of the load but also to resist aerothermal flux. In this study, the aerothermal flux of 160–110 km was calculated, and the loop heat pipe (LHP) coupled with a thermo-electric cooler (TEC) and multi-layer insulation (MLI) were applied to ultra-LEO satellites to determine the variable switching and fast response of heat dissipation and heat protection. An aerothermal flux simulation test platform was built. After the assessment of the ultra-LEO aerothermal flux test, even when the head temperature was as high as 350 °C and the side radiator temperature was as high as 160 °C, the temperature of the internal heat source could be controlled within 22.5 °C through the efficient work of the thermal variable switch system. The study confirms the accuracy and feasibility of the system, which provides an important reference for the subsequent actual on-orbit mission.

With the continuous advancement of on-orbit services and space intelligence sensing technologies, the efficient and accurate identification of spacecraft components has become increasingly critical. However, complex lighting conditions, background interference, and limited onboard computing resources present significant challenges to existing segmentation algorithms. To address these challenges, this paper proposes a lightweight spacecraft component segmentation framework for on-orbit applications, termed M4MLF-YOLO. Based on the YOLOv5 architecture, we propose a refined lightweight design strategy that aims to balance segmentation accuracy and resource consumption in satellite-based scenarios. MobileNetV4 is adopted as the backbone network to minimize computational overhead. Additionally, a Multi-Scale Fourier Adaptive Calibration Module (MFAC) is designed to enhance multi-scale feature modeling and boundary discrimination capabilities in the frequency domain. We also introduce a Linear Deformable Convolution (LDConv) to explicitly control the spatial sampling span and distribution of the convolution kernel, thereby linearly adjusting the receptive field coverage range to improve feature extraction capabilities while effectively reducing computational costs. Furthermore, the efficient C3-Faster module is integrated to enhance channel interaction and feature fusion efficiency. A high-quality spacecraft image dataset, comprising both real and synthetic images, was constructed, covering various backgrounds and component types, including solar panels, antennas, payload instruments, thrusters, and optical payloads. Environment-aware preprocessing and enhancement strategies were applied to improve model robustness. Experimental results demonstrate that M4MLF-YOLO achieves excellent segmentation performance while maintaining low model complexity, with precision reaching 95.1% and recall reaching 88.3%, representing improvements of 1.9% and 3.9% over YOLOv5s, respectively. The mAP@0.5 also reached 93.4%. In terms of lightweight design, the model parameter count and computational complexity were reduced by 36.5% and 24.6%, respectively. These results validate that the proposed method significantly enhances deployment efficiency while preserving segmentation accuracy, showcasing promising potential for satellite-based visual perception applications.

Quantum key distribution constellation is the key to achieve global quantum networking. However, the networking feasibility of quantum constellation that combines satellite-to-ground accesses selection and inter-satellite routing is faced with a lack of research. In this paper, satellite-to-ground accesses selection is modeled as problems to find the longest paths in directed acyclic graphs. The inter-satellite routing is interpreted as problems to find a maximum flow in graph theory. As far as we know, the above problems are initially understood from the perspective of graph theory. Corresponding algorithms to solve the problems are provided. Although the classical discrete variable quantum key distribution protocol, i.e., BB84 protocol, is applied in simulation, the methods proposed in our paper can also be used to solve other secure key distributions. The simulation results of a low-Earth-orbit constellation scenario show that the Sun is the leading factor in restricting the networking. Due to the solar influence, inter-planar links block the network periodically and, thus, the inter-continental delivery of keys is restricted significantly.