A distributed integral sliding mode attitude coordination control method is proposed for the attitude coordination regulation problem of formation systems under time-varying topology conditions by introducing integral sliding mode control. Firstly, a distributed sliding mode observer with integral sliding mode is improved to realize autonomous reference attitude estimation of formation members in communication interference environment. Then, a distributed integral sliding mode controller is designed using the integral sliding surface. Its stability is proved by derivation with the homogeneity theory and the Lyapunov stability theory to achieve collaborative tracking control of the reference attitude by each formation member. The numerical simulation outcomes validate that the proposed approach enables rapid and precise coordinated synchronization of the six-satellite SAR formation's attitudes with the reference attitudes. The coordinated tracking control method based on the combined application of a distributed sliding mode estimation and a distributed integral sliding mode controller enables fast and high-precision coordinated attitude tracking for spacecraft formation systems under time-varying topology conditions.

With the rapid increase in the number of spacecraft in orbit, the growing complexity and diversity of mission requirements and space environment, fault diagnosis and recovery are critical for ensuring spacecraft safety and sustainable operations. However, existing fault diagnosis and recovery methods are typically designed for specific fault types and are stored as fixed code on board, often requiring substantial manual intervention and software maintenance, especially in the event of unforeseen in-orbit failures. These methods are still far from achieving full autonomy. To address this issue and enhance the spacecraft's resilience to unknown faults, this paper proposes an Event-triggered Adaptive Fault Diagnosis and Recovery (EAFDR) framework. EAFDR is based on fault event trees and employs a hierarchical fault event analysis technique, prioritizing fault event trees by severity for diagnosis. It then generates fault recovery strategies through Event-Condition-Action (ECA) rules, enabling real-time responses and dynamic monitoring during recovery to ensure system safety. Furthermore, EAFDR decouples the fault diagnosis and recovery processes from the control cycle, allowing for dynamic modification and maintenance of the fault event tree and ECA rules. Ground-based simulation results from a real-world system demonstrate that EAFDR can diagnose and recover from spacecraft faults in orbit with acceptable computational overhead, providing effective support for the safe and stable operation of spacecraft.

Aiming at the high-precision control of composite formation flying for distributed space imaging systems and high actuator collision risk due to small non-contact gaps of disturbance-free payloads， a precision payload formation control of Leader-Follower configuration for composite formation flying is proposed. First， the service module and payload module models of the Leader and Followers based on disturbance-free payload were established. Subsequently， a predictive model of precision payload formation was derived. The modeling predictive controller considering output constraints and non-contact actuator displacement constraints was designed. A rolling optimization approach was employed to solve for the output increment of the non-contact actuators. Thereby， the high-precision formation control of the payload modules was achieved and non-contact actuators were limited in the stroke to avoid module collision. Finally， the proposed controller was verified and compared with LQR controllers through simulations.The simulation results show that the proposed controller achieves a payload formation precision of 2.25μm for relative displacement and 2.07μrad for relative attitude， representing an improvement of up to 82% over the LQR controller. The proposed precision payload formation control based on model prediction provides a technical reference for the engineering applications of distributed space imaging systems.

Space nuclear reactor power sources with a capacity of 100 kWe or more offer advantages such as high energy density, strong autonomy, and long operational lifespan. They are ideal power sources for future missions such as manned interstellar exploration, planetary resource exploitation, and high-resolution Earth observation at high-orbit. A direct cycle system coupling a gas-cooled reactor with a helium-xenon closed Brayton system is one of the preferred technologies for high-power space nuclear reactor power sources. This paper described the system components and working principles of this technology. Combining the R&D process and achievements of China's high-temperature gas-cooled reactor technology, it introduced the technological foundations that might be applied to space gas-cooled nuclear reactor power systems, including Tri-layered isotropic coated particle fuel, inert gas processes, and gas turbine cycle power generation. Considering the special scenarios of space applications, the paper summarized the main challenges and issues faced by high-power space gas-cooled nuclear reactor power sources, followed by the analyses of the key technologies and the related ideas or views involved in the R&D process. Based on this, some preliminary thoughts and suggestions were proposed to provide a support for the development of high-power space gas-cooled nuclear reactor power sources. 

Rydberg atom microwave detection is a new microwave detection technology that has emerged in recent years. Compared with the traditional dipole antenna microwave receiving technology, it has outstanding advantages such as high detection sensitivity, wide spectrum coverage, and front-end miniaturization. It has aroused widespread interest in related fields such as wireless communications, radar detection, and spectrum sensing. It is currently transitioning from laboratory research to engineering application research. This paper summarizes and analyzes the recent progress of Rydberg atom microwave detection technology and looks forward to its subsequent development and future aerospace applications. Firstly, the basic principle of Rydberg atomic microwave detection technology is briefly introduced; secondly, the research progress of Rydberg atomic microwave detection technology at home and abroad is reviewed from four aspects: improving sensitivity, extending frequency range, enriching detection parameters, and practical communication, and its characteristics and advantages over traditional receivers are demonstrated; thirdly, by summarizing the research project layout of Rydberg atomic microwave detection technology, the development difficulty of different technical characteristics is analyzed, and it is pointed out that the characteristics of ultra-wide spectrum coverage and miniaturized front end are easier to develop in the short and medium term, while the more disruptive extreme sensitivity characteristics are more suitable as long-term goals; finally, from the perspective of aerospace applications, the application prospects of Rydbergatomic microwave detection technology in earth remote sensing, electromagnetic spectrum situational awareness and Anti-interference communication are prospected, and the different technical difficulties existing in various applications are discussed respectively.

The study investigates the high-precision application requirements of spacetime reference systems in lunar-Earth space exploration, aiming to evaluate the impact of different spacetime reference systems on the determination of target orbits in lunar-Earth space. Typical orbits within lunar-Earth space are selected as the subjects for quantitative assessment. A comparison strategy for the influence of spacetime reference systems is established. Differences in observational modeling under the frameworks of the Geocentric Celestial Reference System (GCRS) and Barycentric Celestial Reference System (BCRS) are analyzed. Simulation techniques are employed to further explore the specific impacts of these two spacetime reference systems on the determination of target orbits in lunar-Earth space. The results indicate that, under different spacetime reference systems, the modeling differences for ground-based ranging and time delay are less than 4cm, and velocity measurement differences are less than 0.5mm/s. The impact of orbit determination biases remaines within 1m. Modeling differences for inter-satellite bidirectional links are on the centimeter scale, whereas modeling differences for unidirectional links can reach 10m. For lunar-Earth space target orbit determination tasks requiring accuracy at the decimeter level, the application of GCRS is sufficient to meet demands. However, for achieving higher precision in orbit determination, particularly in constructing and maintaining high-precision spacetime benchmarks in lunar-Earth space, as well as utilizing inter-satellite unidirectional link data, the adoption of the BCRS is more appropriate.

To protect the security of spacecraft information and evade the detection and monitoring by surveillance satellites, this paper proposed a method for planning the covert maneuvering trajectory of spacecraft cluster based on the field angle constraint of surveillance satellites. This paper presented the mission scenario of spacecraft cluster covert maneuvering and the relevant equations for spacecraft cluster trajectory planning firstly, and then summarized the concept and specific steps of transforming the continuous optimal control problem into a nonlinear programming problem using the Radau pseudo-spectrum method. Based on this foundation, we developed a method for planning the covert maneuvering trajectory of a spacecraft cluster. This method took into account both fixed and non-fixed sensor installations, combined with the detection characteristics of optical sensors. The approach ensures that the spacecraft cluster evades detection by navigating around the surveillance area of the satellites. Finally, the validity of the proposed algorithm is verified through comprehensive numerical simulation. The minimum line of sight angle between the cluster members and the surveillance satellite is greater than the half-field angle of the surveillance satellite, and the constraints of safe distance and maneuverability are fulfilled. The spacecraft cluster successfully accomplishes the scheduled space mission and evades the detection of the surveillance satellite. Compared with the trajectory planning results of shape method, the advantages of the proposed algorithm in energy consumption are demonstrated. The covert maneuvering trajectory planning method for spacecraft cluster proposed in this paper can effectively prevent the detection and monitoring by surveillance satellites and enable spacecraft without covert capabilities to achieve the covert effect.

To investigate the thermally induced vibration problem of solar sail in tandem caused by the alternating thermal environments during the period of entering and exiting the Earth’s shadow, a rigid-flexible-thermal coupling dynamic modeling method is proposed. Firstly, the dynamic variational equations and heat conduction equations of the flexible solar plate are derived, and then the multi-physics coupling dynamic model of solar sail in tandem is established based on forward recursive formulation, considering the influences of the attitude angles of central rigid-body and solar plates, elastic deformations, and the Earth’s shadow on the solar radiation heat flux. The use of the joint relative coordinates and the modal coordinates can reduce the system degrees of freedom, such that the computational efficiency is improved a lot. Simulation results indicate that when the spacecraft enters and leaves the Earth’s shadow, significant vibrations of the central rigid-body angular velocity and the solar plate deflection are induced, and that the vibration amplitude decreases with the increase of the convective heat transfer coefficient. The stable temperature after the attenuation of the vibration approaches the environment temperature. The proposed method reveals the influence of multi-source coupled vibration on the stability of system motion, which lays a foundation for the attitude control of solar sail in tandem in variable temperature environment.

The centroid measurement accuracy and computational efficiency of star spots in star charts are key performance indicators for star sensors in space. This study aims to develop a deep learning-based centroid measurement method (Deep Learning-based Centroid Measurement, DLCM) to address the limitations of traditional centroid measurement methods in terms of accuracy and computational efficiency, particularly under noisy conditions and complex star charts. The DLCM method utilizes convolutional neural networks (CNN) to automatically extract complex features from star charts, and employs multiple fully connected layers in the output layer of the network to predict the centroid position through regression. To train the neural network, Gaussian spots under various noise levels are simulated, and the network structure is optimized using a large volume of training data. The DLCM method adapts to varying noise levels and image conditions without requiring manual parameter adjustments or preprocessing based on image characteristics. Experimental results demonstrate that the DLCM method achieves a centroid measurement accuracy of 0.05 pixels within a 3σ range, with excellent robustness and generalization capabilities. Furthermore, DLCM shows significant advantages in computational efficiency. The experimental results validate the potential application of DLCM in star chart centroid measurement, showcasing its high accuracy and efficiency. This method provides effective technical support for the development of future high-precision star sensors and other electro-optical pointing measurement devices.

Aiming at the challenge of monitoring fatigue cracks in spacecraft structures during service, an intelligent identification method for fatigue cracks based on fiber Bragg gratings and ultrasonic guided waves was proposed in this paper. Firstly, a hybrid approach combining passive monitoring using fiber sensing with active detection via ultrasonic guided waves was employed to comprehensively acquire the state data of spacecraft structures. Then, a deep learning network of stacked denoising autoencoders was adopted to construct a fatigue crack identification model for structures, which could adaptively extract crack damage features directly from the structural state data and represent the complex mapping relationship between the structural states and fatigue cracks with a deep model, achieving precise identification of crack locations and lengths. Experimental results show that the identification accuracy of fatigue cracks in the proposed method is more than 90%, which can meet the application requirement of autonomous identification of fatigue cracks in on-orbit spacecraft structures.

To meet the force sensing requirements of space robots in medium and highspeed operating environments, a six-axis force/torque sensor for space robot is developed to improve its dynamic performance. Based on the analysis of the static and dynamic characteristics of the sensor, a multi-objective optimization model focusing on dynamic characteristics for the structural parameters is established, which also considers quality, stiffness, strength, and isotropy in a friendly manner. This model involves 11 parameters to be optimized. By means of chaotic map, nonlinear parameter a and linear alpha wolf weight, MOGWO is improved. Based on this, the optimized process combining IMOGWO with FEM is established. Then the Pareto solution set of the optimal structure parameter is obtained. In order to scientifically evaluate the solution set, the comprehensive evaluation framework is established by using TOPSIS and CRITIC method. Finally, the optimal structural parameters are obtained. The simulation and experimental performances of the optimized sensor are analyzed and compared. The results show that the quality of the improved sensor decreases by 5.9%. Meanwhile, the stiffness increases by 7.7%-22.7%, the strength increases by 5.4%-26.9%, the isotropy increases by 4%, the natural frequency increases by 11.1%-35.7%, and the amplitude decreases by 19.4%. This comprehensive analysis method, which combines IMOGWO, FEM, TOPSIS and CRITIC method, has certain engineering guiding significance for solving multi-objective and multi-parameter structural optimization problems.

Autonomous planetary landing navigation based on sequential images is constrained by the limited computational resources onboard the lander, making it difficult to process and match a large number of image features. This paper designs observability degrees from both spatial and temporal dimensions. A spatial configuration observability degree for landmarks is introduced to optimize the selection of the best feature locations within the sequence, while a depth estimation error observability degree is proposed to determine the optimal time intervals between features. These observability degrees are used to guide the construction of lightweight sequential features with low computational burden. The proposed observability degrees avoid complex matrix operations, making them suitable for onboard autonomous selection of landmarks and feature sequences. Simulation results demonstrate that, compared to traditional methods, the lightweight sequential feature construction method significantly reduces computational load and the number of image processing operations during landmark selection, thereby effectively enhancing the autonomous navigation capability of the lander.

To address insufficient hysteresis nonlinearity compensation accuracy and deteriorated dynamic response under complex disturbances in wide-range flow control of microfluidic proportional valves for aerospace electric propulsion storage and feed systems, a composite control algorithm is proposed integrating improved Bouc-Wen hysteresis model feedforward compensation with feedback coordination. An asymmetric rate-dependent Bouc-Wen hysteresis model is developed by incorporating dynamic rate compensation terms and asymmetric hysteresis operators to reconstruct hysteresis loops. A modified particle swarm optimization (IPSO) algorithm is implemented for parameter identification, reducing mean square error by 43.2% compared with standard PSO. A feedforward-feedback composite controller is designed based on this model, synergistically optimizing hysteresis compensation and PI error correction. Experimental validation demonstrates that step response time is reduced from 1.1s (PI control) to 0.425s (61.4% improvement), that root mean square error in square-wave tracking decreases from 1.83kPa to 1.2kPa (34.4% reduction), and that recovery time under 350kPa step disturbance reaches 0.24s with overshoot consistently below 0.8%. The proposed improved Bouc-Wen hysteresis modeling and dynamic gain hybrid control method addresses the critical challenge of synergistic optimization between precise nonlinear compensation and dynamic robustness. Compared with existing composite strategies, dynamic response speed increases by over 30%, providing a high-precision (steady-state error <1%), strong anti-disturbance (overshoot <1%) engineering solution for spacecraft propulsion systems across wide operational domains.

The low earth orbit (LEO) navigation augmentation system is currently a hot topic in the field of global satellite navigation system (GNSS), and its main development direction is to accelerate the convergence speed of precise point positioning (PPP) by broadcasting dedicated LEO navigation augmentation signal. However, LEO satellites have lower orbital heights and faster motion speeds, resulting in a Doppler range of over ±30kHz for LEO navigation augmentation signal, much higher than the ±5kHz range of traditional GNSS signals. How to fast acquire the LEO navigation augmentation signal has become a challenge. An LEO navigation augmentation signal with anti-Doppler characteristics is proposed to address this issue. The proposed signal has the constant envelope characteristic, which is beneficial for signal generation. More importantly, the proposed signal exhibits the multi-peak correlation properties in the Doppler dimension, enabling fast acquisition. For the proposed signal, a two-step method is designed to achieve fast acquisition: the first step is to acquire the signal within the ±5kHz Doppler range, and the second step is to utilize the multi peak correlation characteristics of the Doppler dimension to determine the ambiguity of the Doppler dimension and obtain the final Doppler and code phase. The simulation results show that the designed LEO navigation augmentation signal only needs to search for the Doppler dimension within ±5kHz range during the acquisition phase, which is more than 6 times faster than the acquisition speed of the conventional LEO navigation augmentation signal, and that the acquisition efficiency is comparable to traditional GNSS signals. The proposed LEO navigation augmentation signal has the anti-Doppler characteristics, and its acquisition time does not change with the Doppler range. It can provide reference for the design of LEO navigation augmentation signal.

With the increasing intensity of radar electronic warfare and the growing complexity of practical electromagnetic battlefields, there is an urgent need to enhance the suppression capability against intermittently sampled repeater jamming (ISRJ) under more challenging echo signal environments. This study proposes an amplitude preprocessing-based ISRJ suppression method. Prior to filtering analysis, envelope detection is first performed on the amplitude of jamming-contaminated echo signals. Through analyzing the variation characteristics of envelope differential values, jamming-free segments are accurately extracted. Subsequently, amplitude compression is implemented on the contaminated signals based on the amplitude characteristics of these clean segments. Finally, energy analysis-based frequency-domain filtering is applied to the preprocessed echo signals. Multiple simulation experiments are conducted to validate the algorithm's ISRJ suppression performance under various signal-to-noise ratio (SNR) and interference-to-signal ratio (ISR) scenarios. Theoretical analysis and simulation results demonstrate that the proposed algorithm significantly enhances radar systems'ISRJ suppression capability. Under low SNR (5dB) and low JSR (5dB) conditions, the signal-to-interference ratio improvement factor in pulse compression results exceeds 30dB. In high SNR (10dB and 20dB) and high ISR (10dB and 20dB) scenarios, this improvement factor surpasses 55dB, showing markedly superior performance compared with two classical algorithms. The proposed algorithm demonstrates strong engineering practicality and provides valuable insights for developing novel intra-pulse deception jamming suppression techniques. It offers significant reference value for both the engineering development of countermeasure prototypes and the implementation of electronic counter-countermeasure tactics.

Spatial channel recognition is a crucial technology in Non-Terrestrial Networks (NTN) communication, providing prior information to meet communication needs in different environments. Due to the complex and rapidly changing propagation environment of long-distance satellite communication, existing channel recognition technologies that only use amplitude features struggle to accurately identify NTN channel types, resulting in low recognition accuracy.To address this issue, a Cascade Merge Convolutional Network (CMC-Net) using the channel impulse response complex signal is proposed. This approach captures independent and joint spatiotemporal features of IQ components without increasing model parameters, which improves NTN channel recognition performance and information utilization. Initially, a one-dimensional convolution extracts local spatial features from each IQ component, which are then combined via a concatenation layer followed by further feature extraction and compression via two cascaded convolution modules. Finally, a temporal classification module is employed to capture global temporal features, leading to the recognition result. Simulation results show that, compared to channel recognition methods using amplitude features, the use of channel IQ data improves the average recognition accuracy by 0.99%, 2.99%, and 8.25% for CNN, CNN-LSTM, and MLP classifiers, respectively. The proposed CMC-Net achieves an average recognition accuracy of 98.15% in the 0～20dB SNR range. In the -20～20dB SNR range, the CMC-Net method improves recognition accuracy by 32.77%, 4.07%, 3.48%, and 1.26% compared to LSTM, CNN, MLP, and CNN-LSTM classifiers, respectively.The results demonstrate that using channel impulse response complex signals effectively improves NTN channel recognition accuracy. The proposed CMC-Net method achieves higher accuracy for NTN channel recognition with fewer parameters, which expands the application of channel recognition technology in the satellite domain.