Browse Topic: Electrical, Electronics, and Avionics

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Deriving Representative Head-Impact Profiles in Contact Sports: A Shape-Based Clustering Approach09-14-03-0018To be published on 10/26/2026
Instrumented mouthguards (iMGs) capture thousands of head acceleration events in contact sports, though processing the data from these into meaningful information remains a challenge. To bridge this gap, we developed a principled clustering pipeline to extract concise kinematics that are representative of real-world impact scenarios. We evaluated six multivariate time-series distance metrics using k-medoids clustering and a dataset of 5149 impacts across three sports: mixed martial arts, boxing, and rugby union. Model performance was evaluated using a Coverage Quality Score (CQS), prioritizing reconstruction coverage and cluster separation. Joint Shape-Based Distance (SBD) significantly outperformed all other metrics, including Dynamic Time Warping (DTW), driven primarily by superior reconstruction coverage. The methodology yielded two complementary archetypes. First, a 36-impact canonical set provides distinct medoids representing core kinematic mechanisms; however, distributional checks confirm these medoids alone do not reproduce cohort severity distributions. To resolve this, we generated a second, extended impact set sampled proportionally within clusters along specific severity axes (e.g., peak acceleration and whole-brain strain). This targeted sampling recovers full severity-distribution fidelity using fewer than 150 impacts per dataset. Ultimately, this framework successfully distills large iMG datasets into manageable cohorts providing a scalable solution for assessment, experimental targets, and finite element simulations.
Koumantou, Eleni, Chan, Emily Yik Kwan, Jones, Chris M., Austin, Kieran, Loosemore, Mike, Ghajari, Mazdak
Associations between Vehicle Safety Systems and Pilon Fracture Severity Are Revealed by Using a Novel Objective Quantitative Fracture Energy Metric09-14-03-0013To be published on 10/26/2026
Pilon fractures are a severe lower-extremity injury sustained in motor vehicle crashes, characterized by high-axial loading that drives the talus into the tibial plafond and frequently results in poor long-term functional outcomes and post-traumatic osteoarthritis (PTOA). Despite advances in surgical management, clinical complications remain frequent in 20–50% of cases in literature, which highlights the need to identify modifiable upstream risk factors in the motor vehicle crash environment. The Abbreviated Injury Scale (AIS) is the predominant tool for crash injury severity analysis, and it classifies all closed pilon fractures uniformly as moderate (AIS 2) regardless of comminution or imparted energy and poorly predicts long-term functional outcomes. An existing fracture energy severity metric quantifies the acute mechanical energy delivered to bone during fracture and has been linked to PTOA development. This study represents one of the first multivariate applications of the fracture energy metric to crash data, examining how occupant demographics, crash kinematics, and vehicle safety systems relate to pilon fracture severity. Fifty belted occupants with Orthopaedic Trauma Association (OTA) Type B or Type C pilon fractures were identified through the Crash Injury Research and Engineering Network (CIREN) database. Fracture fragment surface models were generated from clinical computed tomography (CT) and used to compute fracture energy severity through a custom MATLAB script. A generalized linear model with a gamma log link was fit using seven predictors: age, delta-v, knee bolster airbag (KBAB) deployment, fracture loading mechanism, biological sex, osteoporosis, and body mass index (BMI). KBAB deployment and pure axial compression loading were both significantly associated with higher fracture energy (p = 0.011 and p = 0.002), and age approached an inverse association (p = 0.086). These findings establish the fracture energy metric as a viable tool for crash injury analysis and suggest that certain vehicle safety system characteristics warrant further investigation as modifiable factors influencing pilon fracture severity.
Northrup, Samuel, Bangert, L. Garrett, Armstrong, William, Stitzel, Joel D., Martin, R. Shayn, Dibbern, Kevin, Anderson, Donald D., Weaver, Ashley A., Collins, Caitlyn J.
Suppression of Traffic Oscillations by Vehicular Platoons with Periodic Control Configuration12-10-01-000310/21/2026
Traffic oscillations degrade the operational efficiency and safety of vehicular platoons. This study proposes a predecessor-following platoon with periodically assigned feedback gains and investigates its frequency-selective disturbance attenuation mechanism. An auxiliary second-order undamped periodic model is first established, and the Bloch–Floquet formulation is used to derive the nominal dispersion relation and bandgap boundaries. Closed-loop disturbance attenuation is then evaluated separately using spacing-error transfer matrices that retain positive velocity feedback and actuator-response lag. Frequency-domain transmission analysis and time-domain simulations show that, under nominal homogeneous and fully controllable connected and automated vehicle (CAV) assumptions, the periodic gain configuration creates tunable attenuation bands and provides stronger downstream attenuation than a homogeneous-gain configuration. The results clarify the relationship among periodic gain assignment, bandgap formation, periodic-cell string stability, and finite-length traffic-wave suppression. The effects of vehicle heterogeneity, sensing delay, and mixed traffic remain topics for future investigation.
Yang, Xiujian, Fan, Yanwen, Zhuang, Qingyuan, Qiao, Feiyu
LLM-Based Satellite Multimodal Knowledge Graph Construction: A Context-Aware Self-Interpretable Paradigm2026-99-290410/19/2026
This paper addresses three main bottlenecks in commercial aerospace multimodal data: semantic alignment difficulties, rigid ontologies, and context loss. We propose a method to construct satellite multimodal knowledge graphs (KGs) using Large Language Model (LLM).The process begins with layout analysis. This step preprocesses interleaved text-image satellite documents to align cross-modal semantics. To resolve ambiguity in technical terms, we developed a triple-positioning entity extraction mechanism. This mechanism directly identifies the original text, summaries, and entity types. For event nodes, we use an event-centric modeling paradigm. This paradigm captures spatio-temporal elements and other meta informations to ensure the nodes are self-interpretable. Entity normalization and automatic schema recognition rely on deep vector embedding. This embedding can semantic clustering the entities for the KG, eliminates the need for prior ontologies. We tested the method on real satellite datasets. The experimental results show high logical integrity and confirm its practical value for aerospace failure analysis.
Chen, Yanyu, Yu, Yeluo, Zhang, Jiliang, Bao, Junpeng
System-Level Interference Analysis between LEO Mega-Constellations2026-99-291610/19/2026
With the large-scale construction of low Earth orbit (LEO) mega-constellations (such as Starlink, OneWeb, etc.) worldwide, due to the scarcity of frequency bands and the increasingly tight orbital space, the problems of mutual influence within constellations and between different constellations have become increasingly serious. To solve these problems, this paper studies a multi-level Walker-Delta layout method for LEO constellations. Based on the consideration of the relationship between time and space, a global interference calculation method is proposed. Combining orbital mechanics and the direction pattern of antennas, the interference amount generated by satellites at different orbital altitudes on ground relay stations is given. Simulations show that the interference is not the same everywhere, there are certain differences in latitude, and it also varies in time. Especially for high-latitude regions, the signal-to-interference-noise ratio will drop significantly and even fall below 5 dB; while in mid-low latitude regions, the situation is good. This has certain guiding significance for spectrum sharing coordination and anti-interference beam formation.
Zi, Xinyi, Li, Haiyang, Zhang, Jin, Li, Shengxi
Multilevel Bayesian Model Updating for Satellite High-Stability Brackets Considering Welding Uncertainties2026-99-292510/19/2026
The high-stability bracket is the primary component in remote sensing satellites responsible for carrying the camera and attitude control equipment; its dynamic behavior directly affects imaging quality and in-orbit service life. Welding-based connections introduce uncertainties during manufacturing and assembly, leading to significant discrepancies between finite element analysis results and experimental data. To quantitatively assess the impact of welding processes on the accuracy of the high-stability bracket’s finite element model(FEM), this study proposes a Bayesian parameter correction method based on Markov Chain Monte Carlo (MCMC) sampling and the Metropolis-Hastings (MH) algorithm. It employs a multilevel correction experimental framework—”material-level, component-level, and system-level”—to progressively refine material parameters and establish a high-fidelity FEM. First, the elastic modulus of the welded and non-welded regions were calculated using stress-strain curves obtained from static tensile tests on welded and non-welded plates; then, targeting the first two natural frequencies from dynamic impact tests on bar specimens, the material densities of the welded and non-welded regions were corrected using the Bayesian method; finally, the effectiveness of the correction method was verified through free vibration tests on the actual high-stability bracket. The results indicate that, compared to the undamaged region, the welded region exhibits a 13.87% loss in elastic modulus and a 55.32% loss in density. The relative errors in the first three natural frequencies of the corrected model were 0.59%, 0.01%, and 0.83%, respectively, verifying the high reliability of the proposed method in modal identification and providing a new technical approach for correcting spacecraft structural models.
Liu, Kun, Guo, Chongchong, Wu, Wenhua
Modeling and Analysis of Mechanical-Electrical Coupling in Flywheel Systems Considering Faults2026-99-292310/19/2026
Spacecraft flywheel systems are critical actuators for attitude control and stability maintenance, and their operating reliability is strongly affected by the health state of the supporting bearings. When local defects occur on the bearing raceway or rolling elements, the rolling contact state changes abruptly, which leads to time varying contact deformation, fluctuating support reaction force, and additional rotational resistance. Unlike many ground based rotating machines, spacecraft flywheel systems usually operate in compact and highly integrated configurations, where direct vibration measurement is difficult in practice. In contrast, motor current, voltage, and speed signals are easier to acquire and are therefore more suitable for condition monitoring. To describe how bearing defects propagate from the mechanical domain to electrical responses, a simplified electromechanical coupling model is developed for a flywheel rotor motor system with local bearing defects. First, a rolling element contact model is established using the defect induced deformation relation and Hertzian contact theory to derive the time varying bearing support force. Second, the fluctuating support force is mapped to an equivalent time varying load torque to represent the dominant pathway through which bearing defects influence the motor side. Third, the derived load torque disturbance is coupled with the transverse rotor dynamic equations, the rotational motion equation, and the motor dq axis model to obtain a simplified electromechanical coupling equation set. The proposed model preserves the main physical transmission chain from bearing defect to electrical response. The model provides a clear theoretical basis for analyzing fault propagation mechanisms and for developing current based diagnostic methods for spacecraft flywheel systems.
Wu, Haolong, Li, Yuqing
Satellite Autonomous Task Decision-Making Method Based on Attention Pointer Network2026-99-296510/19/2026
Large-scale satellite constellation on-orbit missions are characterized by multiple objectives, multiple constraints, and strong dynamism. Traditional ground control and management modes have become difficult to meet the requirements for autonomous real-time decision-making by satellites. This paper addresses the need for autonomous and rapid decision-making capabilities on-board satellites, and proposes a satellite autonomous task planning and decision-making method based on an attention pointer network, addressing issues such as limited on-orbit computing resources and insufficient generalization of planning methods. Firstly, by analyzing and mathematically describing the characteristics and constraints of satellite tasks, a Markov model for satellite autonomous task planning problems is constructed. Secondly, the pointer network is combined with a reinforcement learning framework, and an attention mechanism is introduced to perform probabilistic learning on the execution selection of satellite tasks, generating an optimized sequence of satellite task planning results. Finally, simulation experiments show that after training, this method exhibits certain generalization capabilities and significantly superior computational efficiency compared to traditional intelligent optimization algorithms such as genetic algorithms. It can adapt to the single-satellite autonomous dynamic planning requirements under large-scale constellation control and management, providing technical support for autonomous operation and online intelligent task planning of satellites in orbit.
Feng, Xiaoen, Li, Yuqing, Qiu, Jianbin, Li, Meicheng
Active Control of Attitude-Maneuvering-Induced Vibration of Membrane Antenna Based on Reinforcement Learning2026-99-290810/19/2026
Space membrane structures, prized for their lightness, affordability, and ability to fold and unfold, hold immense promise for missions like deep space exploration, high-definition Earth observation, and advanced satellite communications. However, these structures are typically large-size, ultra-lightweight, highly flexible, and have very low damping. This leads to complex dynamic behavior featuring significant geometric nonlinearity, strong coupling between rigid and flexible motions, and uncertain parameters. Consequently, creating accurate yet simple mathematical models for designing controllers is exceptionally difficult, and traditional control methods that rely on such models often fall short. To tackle this problem, this study focuses on a membrane antenna spacecraft equipped with cable actuators, aiming to suppress its vibrations during attitude maneuvers. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on the Actor-Critic architecture is designed. This approach is data-driven, meaning it learns effective control strategies directly through interaction with the system’s environment, bypassing the need for a precise pre-defined mathematical model. Simulation results show that the controller demonstrated superior vibration suppression and strong adaptability across several key scenarios.
Zhang, Hua, Chen, Cheng, Liu, Xiang
Graphene Solar Sail for LEO Deorbiting Based on Experimental Light Propulsion Modeling2026-99-292610/19/2026
Graphene materials have recently been shown to produce measurable propulsion when exposed to light, raising the possibility of using them to enhance solar sail performance for satellite deorbiting. The pendulum-based experiments on graphene samples under controlled laser illumination inside a vacuum chamber and a quantitative model that describes the propulsion force as a function of laser power, illuminated area, incident angle, and ambient pressure were carried out. The measured pressure dependence and its shift with sample characteristic size point to Knudsen forces as the dominant mechanism. Orbital dynamics simulations incorporating this model show that a graphene-based sail can deorbit a 100 kg satellite from 660 km altitude roughly 21% faster than a conventional membrane sail of the same area, because the graphene configuration combines atmospheric drag with light-induced propulsion in a hybrid decay process.
Zhu, Haidong, Liu, Xiang, Cai, Guoping
Nonlinear Dynamic Modeling and Active Control of a Large-Scale Two-Dimensional Planar Phased Array Antenna2026-99-290110/19/2026
A new method for dynamic modeling and active control of the large-scale, on-orbit assembled two-dimensional planar phased array antenna is proposed in this study. As the demand for high imaging accuracy and scanning coverage in satellite antennas increases, there is a shift toward larger apertures. However, large-scale phased array antennas face challenges such as low rigidity and significant nonlinear vibration during attitude maneuvers, which can degrade pointing accuracy and shape precision. To address these issues, this study proposes a dynamic modeling framework that incorporates geometric nonlinearity, employing finite element method (FEM) and a polynomial fitting-based model order reduction technique. A rigid-flexible coupling dynamic model is then developed based on the velocity variation principle. In control design, the paper integrates an active vibration control law using cable actuators with a computation torque algorithm based on control moment gyroscopes (CMGs) to design an integrated attitude-vibration control system. This system aims to suppress nonlinear vibration while achieving high-precision attitude control. Simulation results demonstrate the effectiveness of the proposed integrated control approach in both vibration suppression and attitude maneuvering, offering valuable insights for its practical application in future space missions.
Ma, Yihao, Liu, Xiang, Jin, Chaochen, Cai, Guoping
Adaptive Slicing and High-Reliability Transmission Method for Spacecraft On-Orbit Data2026-99-292210/19/2026
Spacecraft On-orbit data are characterized by large volumes, stringent timeliness requirements, and heterogeneous data values, posing significant challenges to traditional satellite-ground transmission systems. To improve transmission efficiency and reliability under dynamic link conditions, this paper proposes an adaptive data slicing and collaborative reliability enhancement method. First, a joint evaluation model integrating Data Impact and Link Health is developed to evaluate data importance and transmission conditions. Based on the evaluation results, the slice size, priority allocation, and redundancy configuration are dynamically optimized. Second, a distributed collaborative verification mechanism combining Reed–Solomon erasure coding and multi-node arbitration is introduced to enhance data integrity and recovery capability. Furthermore, a mission-driven elastic scheduling strategy is designed to facilitate efficient resource allocation in multi-user transmission scenarios. Experiments were conducted on 100 telemetry files with a total volume of 1.9 GB in a simulated satellite-ground communication environment. Experimental results demonstrate that the proposed method improves average throughput by 25.3% and achieves a reconstruction success rate of 99.7% across 1,000 packet-loss reconstruction trials.
Li, Cheng, Liu, Peng, Chen, Xi, Cui, Yan, Li, Han, Li, Xian, Wang, Zhihui, Yi, Xiu
Task Allocation and Trajectory Optimization for Mother-Daughter Satellite Clusters Observing Non-Cooperative Constellations via Improved Genetic Algorithm2026-99-291710/19/2026
Over the past decade, the rapid deployment of Low Earth Orbit (LEO) mega-constellations has significantly reshaped the spatial distribution of the near-Earth environment and increased orbital density to historically high levels. While popularizing space access, the accumulation of defunct or non-cooperative satellites raises severe concerns regarding space sustainability and the Kessler syndrome. Consequently, the ability to conduct active, multi-target close-proximity inspection is becoming an essential capability for Space Situational Awareness (SSA). Traditional monolithic servicing strategies rely on sequential point-to-point orbital transfers, which have to face heavy computation cost and restrictive fuel constraints when targeting multi-planar distributed networks requiring continuous out-of-plane maneuvers and phasing adjustments. In order to overcome these limitations, the paper proposes a coordinated mother-daughter satellite cluster architecture. A primary mother satellite serves as a deployment hub, while a swarm of highly maneuverable daughter satellites executes sequential "Observe-and-Return" missions, gathering high-fidelity SSA data. Assigning these multi-target inspection missions to a limited fleet creates an NP-hard combinatorial challenge, further compounded by strictly non-overlapping operational time windows and rigid propellant boundaries. To navigate this high-dimensional search space, the study introduces an improved Genetic Algorithm (GA) featuring a dynamic chronological decoding mechanism. Furthermore, to eliminate secular positional drifts during orbital integration, a high-fidelity five-impulse maneuver sequence is established, embedding an autonomous Lambert-based positional compensation mechanism. Simulation results indicate that the proposed method yields scalable, fuel-feasible, and load-balanced kinematic baselines for autonomous target characterization across large-scale constellations. Additionally, an event-triggered dynamic re-planning mechanism is utilized to robustly adapt to uncooperative evasive maneuvers in highly dynamic real-world space scenarios.
Sheng, Beining, Tian, Yang, Li, Tieying, Shan, Yiming
Efficient Extrapolation-Based Dynamic Link Prediction for Large-Scale Satellite Networks2026-99-290610/19/2026
Inter-satellite links (ISLs) in large LEO constellations vary quickly with time, and their rapid change makes future link-state prediction a practical concern. Predicting ISLs with reasonable accuracy benefits routing decisions, resource scheduling, and overall network stability. Existing methods are still limited in several ways. Rule-based and physics-based methods build on hand-crafted assumptions, which become unreliable once links flicker frequently or topologies grow heterogeneous. Static graph neural networks describe the relations within a single snapshot, but their formulation does not represent how ISLs evolve from one moment to the next. Many recent dynamic graph models stay close to interpolation, and they exploit physical and temporal satellite attributes only in part, which weakens both node representations and prediction accuracy. To address these gaps, we develop an extrapolation-oriented dynamic link prediction framework with three contributions: (1) we shift the task from interpolation toward future-state extrapolation, which makes proactive management possible; (2) we extract physics- and temporal-aware node features by combining orbital dynamics with historical ISL information; (3) we restrict prediction to a candidate edge set, so the computation stays manageable while the method remains scalable. In experiments, the proposed method outperforms existing baselines on accuracy, efficiency, and scalability, suggesting that it can serve as a useful tool for managing large-scale LEO networks in practice.
Zhao, Jiayi, Fei, Shihan, Bao, Junpeng
A Three-Axis Attitude Measurement Method for Key Spaceborne Components Based on Dual-Path Autocollimation2026-99-291810/19/2026
Aiming to address the attitude deviations of spaceborne optical payloads caused by the coupled effects of extreme on-orbit environments, as well as the limitations of existing on-orbit measurement algorithms, including reference error propagation and the neglect of prism attitude variations, this paper proposes a three-axis attitude measurement method for key spaceborne components based on dual-path autocollimation. The proposed method constructs a two-level spatial attitude measurement chain and employs a low-thermal-expansion polyhedral prism as a unified central reference, thereby transforming the discrete measurement of multiple instruments into a transfer solution relative to a common reference. Focusing on the first-level measurement chain, a dual-path autocollimation measurement method is derived to solve the three-axis attitude angles of the remote prism. The proposed method does not rely on the actual satellite attitude reference and can directly monitor the absolute attitude deviations of spaceborne instruments, thereby laying a foundation for attitude measurement of distributed spaceborne instruments. Analysis of typical layout angles shows that a 90° orthogonal configuration enables complete decoupling of the three attitude angles, whereas a 60° acute-angle configuration is more compatible with the limited space available on a satellite and achieves partial decoupling. Moreover, when the included angle between the two autocollimators lies within 30° to 90°, the solution exhibits favorable stability, meeting the requirements of high-precision spaceborne attitude measurement.
Shi, Jian, Liu, Juncai, Tan, Jiubin
Collaborative Design of Low Earth Orbit Multi-Layer Constellations for Coverage and Networking Performance2026-99-290710/19/2026
With the growth of global communication demand, multi-layer Low Earth Orbit (LEO) satellite constellation has become an important part of the next generation network. The multi-layer hybrid constellation can effectively improve the coverage performance and network connectivity through the cooperative work of satellites with different orbital heights. In this paper, a dynamic model of constellation topology is established for multi-layer LEO constellations. The index system of coverage and networking is constructed by using the strategy of fixed neighbor inter-satellite link (ISL) in the same layer and the nearest neighbor dynamic inter-layer link (ILL) between layers. Through simulation, the performance of single-layer and multi-layer constellation configurations is compared. The influence of design parameters such as orbital altitude, number of ILLs and inter-layer satellite resource allocation on the indexes of multi-layer constellation is analyzed. The simulation results show that the multi-layer constellation is superior to the single-layer constellation in terms of coverage continuity and network capacity with a fixed total number of satellites. As the number of ILLs increases from 1 to 5, the average shortest path length (ASPL) and the average inter-satellite propagation delay decrease, and the network capacity is significantly improved. Reasonable inter-layer resource allocation and link configuration can effectively improve the networking performance of multi-layer constellations.
Wu, Kejie, Chen, Quan, Li, Linhua, Liu, Erjiang
Power Allocation for Uplink RSMA in LEO Satellite Systems2026-99-292110/19/2026
This paper focuses on power allocation for RSMA-enabled uplink transmission in LEO satellite systems. A representative cluster is selected for analysis, where the terminal with the best channel quality serves as the target user and transmits its information by means of two split streams. The remaining users are treated as weak users sharing the uplink access structure. Since satellite-ground links are affected by channel estimation errors and propagation uncertainty, the available channel state information (CSI) may deviate from the actual channel. To improve the reliability of uplink transmission, a bounded CSI error model is adopted, and the decoding conditions for different successive interference cancellation (SIC) stages are formulated in a robust form. On this basis, the target-user rate is optimized under transmit-power and decoding-reliability constraints. The formulated problem is difficult to solve directly because the power variables are coupled with the SINR and rate expressions. Therefore, a successive convex approximation (SCA) method is employed to transform the original problem into a series of solvable convex subproblems. The obtained results confirm that the proposed scheme brings a clear target-user rate gain over the NOMA benchmark under the same simulation setup. The results show the effectiveness of robust RSMA power allocation for uplink interference management in LEO satellite systems.
Yu, Xiaodi, Jin, Hang, Song, Yu, Hong, Tao, Ding, Xiaojin, Zhang, Gengxin
Design and Performance Analysis of a Hilbert Fractal Antenna for Corona Discharge Detection2026-99-290310/19/2026
Fractal antennas have attracted considerable attention due to their compact geometry and favorable electromagnetic characteristics, particularly in broadband and multiband applications. In this paper, a fourth-order Hilbert fractal antenna is designed and analyzed for radio frequency signal reception in the 200-300 MHz band. The design is motivated by the spectral characteristics of electromagnetic radiation generated during corona discharge, whose primary energy is concentrated within this frequency range. The proposed antenna is optimized in terms of return loss and bandwidth to ensure effective signal reception. Simulation results demonstrate that the antenna exhibits satisfactory impedance matching and achieves stable performance within the target frequency band. Based on the optimized design, a prototype is implemented and evaluated through laboratory experiments. Experimental results indicate that the proposed antenna can effectively capture radio frequency signals associated with corona discharge, demonstrating reliable non-contact detection capability. The study provides a feasible antenna design approach for corona discharge monitoring and offers potential applications in avionics system health management and electromagnetic sensing.
Hei, Guoyu, Li, Shidong, Wu, Yang, Su, Zhenxia, Ye, Hongyan
Research on Testing Methods and Practices of on Board Data Handling Subsystem Based on Spacecraft2026-99-294710/19/2026
Subsystem testing, as a crucial engineering phase in the development process of the On Board Data Handling (OBDH) subsystem to ensure product quality, is increasingly valued by engineering organizations. The OBDH subsystem, apart from its characteristics of embedded software and FPGA with strong real-time performance and close hardware relations, also features multiple external interfaces, large communication data volumes, and frequent software changes. These factors highlight the significant challenges in testing the OBDH subsystem, making it essential to develop a theoretical framework and methods to guide the testing process. Following the principles of the Director’s Order No. 7 for full-cycle and multi-dimensional work, and based on the testing characteristics of the OBDH subsystem, a full-cycle testing model for the OBDH subsystem has been established. This paper presents comprehensive research and practical implementation of OBDH subsystem testing from three aspects: test environment establishment, test method implementation, and test experience summarization.
Ye, Zhiling, Zhang, Tao
A Wide-Angle Beam Scanning Algorithm for Reconfigurable Liquid Crystal Phased Arrays in Low Earth Orbit Satellite Acquisition2026-99-294910/19/2026
To satisfy the real-time beam tracking requirement of low earth orbit (LEO) satellite ground terminals under highly dynamic pass conditions, this paper proposes a three-state hierarchical state-machine beam-control algorithm for liquid crystal phased array antennas (LCPAs). The tracking process is divided into three stages. S1 coarse nine-point acquisition is used to rapidly cover a wide pointing-uncertainty region. S2 fine nine-point refinement introduces quadratic-surface fitting to obtain local peak estimation at sub-step resolution. S3 predictive tracking adopts an interacting multiple model Kalman filter (IMM-KF) based on the constant-acceleration (CA) model and the Singer model, and designs an adaptive covariance regulation mechanism for both measurement and process noise. Combined with explicit delay compensation for liquid-crystal response time, the method suppresses pointing lag and error accumulation in high-dynamic intervals. A dual-threshold hysteretic loss-of-lock criterion and a filter-reset mechanism are also integrated to guarantee autonomous link recovery. Simulation results show that, in an LEO pass scenario with a maximum angular rate of about 2 °/s, the proposed method reduces the median 2D RMSE by about 52%, reduces the 90% quantile error by about 54%, and lowers the median average power loss from 0.14 dB to 0.03 dB compared with the traditional scheme.
Tian, Lingrui, Guo, Xiaoxu, Xu, Yao, Chen, An, Zhu, Yaowei, Zhang, Zhizhong
A Fuzzy Initiative-Yielding Mechanism for Space-Air-Ground Cooperative Communication2026-99-294110/19/2026
Space-Air-Ground Integrated Network (SAGIN) emerges as an appealing framework for implementing pervasive connections among diverse layers of space, air, and ground. The scheme relies on satellite deployment for extensive space coverage, Unmanned Aerial Vehicle (UAVs) acting as agile aerial relay stations, and ground robots for performing the final task assignment. Nevertheless, integrating heterogeneous nodes with varying mobility characteristics poses significant challenges: satellite operations are restricted by orbital dynamics, ground robot movements are restricted by topography constraints, whereas UAVs demonstrate flexibility and can establish line-of-sight connections with both satellites and ground robots. Current solutions usually adopt UAVs merely as passive relay nodes, causing inefficient adaptation mechanisms and excessive communication overhead when responding to dynamic perturbations or blocked connections. To overcome the aforementioned challenge, this paper proposes a novel fuzzy initiative-yielding coordination approach assigning two opposite behaviors. Initiative is assigned to UAVs in order to facilitate coordinated operations between layers, whereas Yielding refers to the adaptive response of satellites and ground robots. Contrary to previous research where behavioral assignments follow strict threshold rules, our fuzzy inference considers various parameters such as link quality, node state, and task priority, providing flexible behavioral assignments. Our predictive approach incorporates the Exponentially Weighted Moving Average (EWMA) technique to perform proactive failure avoidance. Based on five comprehensive experiments with 30 independent runs each, our approach obtains similar task completion rates (84.4%), but exhibits significantly lower communication overhead by up to 11 to 147 times compared to baseline methods. Furthermore, the proposed behavioral assignments avoid any coordination conflict (0 vs. 30.8), reduce completion time by 10.1%, maintain robust performance under 20% rule reduction, scale to 150-node mega-constellation scenarios, and exhibit higher fault tolerance under dynamic environments (81.5% vs. 79.8% of CBBA under high stress conditions).
Bao, Jie, Zhang, Yuping, Zhang, Ronghao
Approach for Constructing and Allocating a Health Management Indicator System for Reusable Launch Vehicles2026-99-294510/19/2026
Against the performance degradation and failure risks that reusable launch vehicles (RLV) encounter over multiple mission cycles, together with the absence of a unified quantitative foundation for pre-launch health assessment, fault diagnosis validation, and remaining useful life (RUL) prediction verification, this paper presents a health management (HM) indicator system construction and allocation method tailored for RLV. Consistent with international standards for health management and test engineering, the method uses a “core capability decomposition—hierarchical metric mapping” framework to develop a two-dimensional metric system spanning functional and physical domains. Centered on fault detection, fault isolation, and fault prognosis, the approach breaks down top-level HM requirements into concrete technical indicators at the vehicle, system, and component levels, with explicit definitions, calculation formulas, and validation approaches to ensure engineering measurability and comparability. Integrating system criticality analysis and expert judgment, this work adopts the analytic hierarchy process (AHP) and a weight allocation scheme to decompose system-level indicators to critical subsystems including propulsion and flight control systems. A data-driven validation environment is established using a custom HM prototype and high-fidelity simulation platform. Multi-scenario simulation and fault injection yield labeled operational data for computing subsystem-level performance metrics. Comparing bottom-up aggregated indicators with top-down allocated targets forms a complete engineering closed loop from indicator allocation to system implementation and performance verification. Sensitivity analysis confirms that vehicle-level indicators remain within design thresholds under moderate weight perturbations, demonstrating robustness. Results show the method is feasible and consistent in simulation, offering solid support for the design and validation of RLV health management systems.
Shen, Tingzheng, Wu, Jiaxin, Li, Xiaochao, Qian, Ze, Yu, Jinsong
Low-Cost Ground Station Network Siting for Geostationary Space-to-Ground Laser Communication2026-99-295110/19/2026
The deployment of multi-link ground station networks for geostationary space-to-ground laser communication is constrained not only by local weather conditions but also by infrastructure accessibility and engineering cost. Existing ground station siting studies have mainly emphasized cloud-related meteorological indicators and site diversity, while giving insufficient attention to deployment-oriented cost factors. As a result, meteorologically favorable solutions may still be expensive to implement in practice. To address this issue, this paper investigates the low-cost siting problem of a ground station network under a target network availability constraint and proposes a two-stage method for a multi-link, single-telescope scenario. Under the adopted assumption, each ground station can provide at most one available link at any given time, so the number of available network links is equal to the number of clear-sky available stations. The network availability is therefore defined as the probability that at least a required number of links are simultaneously available. Using half-hourly cloud mask data from the Himawari 8 geostationary meteorological satellite for the year 2023, site-neighborhood cloud metrics and inter-site weather correlation metrics are constructed. A data-center clustering and distance-decay model is then introduced to build an infrastructure impact cost field. On this basis, nationwide optimization is carried out through candidate construction and final subset selection. A baseline method without data-center factors is also evaluated under the same total-cost definition. The results show that, under the same target network availability requirement, the proposed method can effectively reduce total deployment cost. This indicates that incorporating data-center factors during siting improves the engineering tradeoff of multi-link geostationary laser communication ground station networks and provides a reproducible route for low-cost planning under service availability constraints.
Zhang, Chi, Zhao, Juntong, Li, Wenfeng, Zhao, Kanglian
Transformer-Based Short-Arc Relative Motion Pattern Recognition for Non-Cooperative Space Objects2026-99-295310/19/2026
To meet the demands of on-orbit monitoring and space situational awareness, this study proposes a Transformer-based relative motion pattern recognition method based on the self-attention mechanism, addressing the issues of difficulty in accurately characterizing the relative motion patterns of non-cooperative space targets under short-arc observation conditions, strong dependence on prior knowledge in traditional methods, and insufficient stability under noise interference. Firstly, this study constructes a non-cooperative spacecraft motion pattern action data set based on the Clohessy–Wiltshire (C-W) relative motion equation. By designing typical initial conditions of relative motion, trajectory samples including fixed-point following, hovering following, oscillating following, coplanar circling, non-coplanar circling, approaching and receding, etc. of multiple motion patterns are generated, and the relative position and relative velocity sequences in the local orbital coordinate system are used as model inputs. Subsequently, an motion pattern recognition network composed of an input representation module, a Transformer encoding module, and a classification output module is constructed. The trajectory sequence representation is achieved through linear embedding and position encoding, and the multi-head self-attention mechanism is used to capture the global dependencies and key dynamic features between different time steps, thereby completing the discrimination of the target motion pattern category. Finally, model training and testing are carried out based on the constructed data set, and experimental analysis is conducted from three aspects: training convergence, classification performance in the absence of noise, and robustness in the presence of noisy observations. The results show that the proposed method can effectively learn the discriminative features in short arc segment relative motion, and performs well in terms of accuracy, Macro-F1, and confusion matrix; in the presence of Gaussian noise interference, it outperforms the Multi-Layer Perceptron (MLP) baseline model while still maintaining clear category boundaries and high recognition stability.
Mao, Yicheng, Zhang, Zexu, Li, Yingshuo, Song, Zhuo, Zhang, Fan, Liang, Jintang
ARET-ALSTM: Intelligent Beam Prediction and Dynamic Compensation Network for Aerial Base Stations under Non-Stationary Turbulent Conditions2026-99-295410/19/2026
With the unprecedented surge in demand for flexible deployment and ultra-high throughput in next-generation wireless networks, airborne base stations (ABS), particularly unmanned aerial vehicles (UAVs), have become emerged as indispensable mobile communication infrastructure. To facilitate high-gain directional transmission within the millimeter-wave band, ABS frequently employ massive MIMO antenna arrays. However, under random disturbances caused by non-stationary turbulence, the system faces a severe physical bottleneck: weak anti-interference capability due to narrow beamwidth, which can easily lead to a sharp drop in link capacity. Addressing the two major shortcomings of existing deep learning prediction schemes—susceptibility to sensor noise interference and negative capacity performance shifts during stable periods, and response lag due to memory inertia during sudden turbulence periods—this paper proposes an ARET-ALSTM active compensation network based on SNR-aware multimodal attention, agile reset and adaptive event-triggered mechanisms. This network incorporates an event-triggered mechanism governed by a dynamic physical variance threshold, adaptively executing compensation during stable intervals to prevent excessive overshoot and redundant computational overhead. Simultaneously, it employs an agile feature reset mechanism based on an adaptive sliding window for concept drift detection, forcibly discarding old memories and reconstructing attention weights during sudden wind shear, achieving millisecond-level ultra-fast adaptation and directly driving network optimization with the goal of maximizing communication capacity. Simulation experiments show that, under different array configurations ranging from 2×2 to 16×16 and complex flow conditions, ARET-ALSTM successfully overcomes the physical capacity collapse limitation under large antenna scales, and its tracking agility and robustness are significantly better than benchmark algorithms such as Transformer, RNN and traditional Kalman filtering.
Tu, Jiajun, Zhou, Jihua, Li, Yanhang, Zhao, Tao, Huang, Hua, Wen, Yuanbin, Guo, Ziwen
Noise Analysis and Circuit Optimization for the Drive Loop of Resonant Accelerometers2026-99-296710/19/2026
Resonant MEMS accelerometers are core inertial devices in precision measurement fields such as inertial navigation, aerospace, and seismic monitoring, characterized by frequency signal output, strong anti-interference capability, and high-precision potential. With the growing demand for sub-microg-level measurement accuracy in high-end industrial and scientific applications, noise suppression and detection sensitivity optimization have become key research topics for resonant accelerometers. This paper conducts a systematic modeling study on resonant accelerometers, analyzes their mechanical structure and acceleration sensing mechanism based on the force-frequency effect of double-ended tuning forks, and establishes corresponding mathematical models. Non-ideal noise models of the mechanical structure and drive loop are constructed separately, and key noise source analysis identifies mechanical thermal noise and 1/f noise of the front-end charge amplifier as the main contributors to system noise. To solve these problems, a dedicated low-noise charge amplifier is designed for the drive loop. Simulation results show that the input-referred noise of the low-noise operational amplifier used in the charge amplifier reaches 18.804 nV/√Hz @ 50 kHz. When applied to the drive loop and verified through tape-out testing, the system achieves a phase noise better than -119.904 dBc/Hz @ 20 Hz offset from the resonant frequency, with a corresponding acceleration noise density of approximately 0.477 μg/√Hz @ 20 Hz.
Cai, Hebin, Li, Zhaohan, Zhang, Qiukun, Liu, Guowen, Zhang, Wenbo, Yin, Liang, Gao, Naikun, Fu, Qiang
Research on Interconnection Technology of Spacecraft in Cislunar Space2026-99-294310/19/2026
Communication interconnection and interoperability in cislunar space have become an important development direction for low earth orbit satellite internet to extend into outer space. To address the challenges such as the difficulty in cross-domain interconnection of heterogeneous spacecraft, insufficient compatibility of protocols and architectures in the ground station,earth orbit, lunar orbit, lunar surface and extended networks, this paper reviews the current development situation of the US and China’s cislunar space networks, and summarizes six challenges in the aspects of link layer and network layer protocol standardization, addressing and routing, gateway design, information security, and multi-system integration. Based on this, this paper proposes constructing a cislunar space network centered on IP protocols based on the USLP link-layer protocol, which adopts a combination of two-layer switching and three-layer routing addressing mechanism, multi-gateway interconnection, hybrid security architecture, and multi-protocol stack compatibility, and clarifies four key technologies: network protocol architecture, standardized routing gateway, electronic data sheet, and network digital modeling and simulation. An integrated network system covering the five domains is constructed, achieving unified standards, fault tolerance, safe and efficient interconnection and interoperability for cislunar space spacecraft, which can provide core support for human lunar exploration, deep space exploration, and the perception and development of the cislunar space.
Jia, Weisong, Wang, Xiang, Liu, Yusheng, Wang, Luyuan, Sun, Yong, He, Xiongwen, Zhao, Kanglian, Xu, Ke
Research on Satellite Constellation Selection Method for GEO Multi-Satellite Relay Systems2026-99-295010/19/2026
Geostationary Earth Orbit (GEO) satellites, as a crucial infrastructure for global communication and emergency support systems, are highly valued due to their wide coverage, long communication distances, and large transmission capacity. However, the scarcity of orbital slots and the difficulty in quickly identifying available satellites in actual user scenarios have become the main bottlenecks restricting the communication efficiency of multi-satellite relay systems in geostationary orbit. To address these issues, this paper proposes an accurate and efficient method for selecting the set of available satellites for multi-satellite relay applications. We construct a spatial coverage model based on the H3 hierarchical discrete global grid system and a geometric model that characterizes the visible orbital arc of the user station. By comprehensively considering the geographic locations of gateway stations and user stations, satellite beam coverage boundaries, and the visibility constraints imposed by user terminal antenna pointing, the proposed method can accurately screen out qualified satellite sets that satisfy the end-to-end link condition of “gateway–satellite–user station”. Simulation experiments are conducted in the Ku-band to validate the effectiveness of the proposed scheme, with performance metrics including coverage probability, accessible satellite quantity distribution, and selection efficiency evaluated. The results show that the H3-based modeling approach effectively reduces computational complexity while ensuring high accuracy in visibility judgment. The proposed method significantly improves the accuracy and efficiency of satellite selection, and supports dynamic updates in mobile user scenarios. This work presents a reliable satellite selection algorithm, which can be used to support efficient resource scheduling and provide a strong guarantee for GEO multi-satellite relay communication.
Yu, Xinyue, Jiang, Yanzi, Lu, Yi, Hou, Yucheng, Su, Xingyu, He, Wenxi, Zhang, Jie
EAU-Net: An Edge-Aware Enhanced Attention U-Net for Robust SAR Near-Shore Segmentation2026-99-291010/19/2026
Synthetic Aperture Radar (SAR) provides important all-weather images. These images help researchers observe the sea surface. However, separating the coast from the ocean in these images is hard. Near-shore areas face three main issues. The first issue is coherent speckle noise. The second issue is geometric distortion caused by the radar’s side-looking geometry. The third issue is feature aliasing caused by multiple scattering from coastal buildings. To solve these problems, this paper proposes the Enhanced Attention U-Net (EAU-Net). This model is a deep learning framework. It adds SAR scattering priors directly into the learning process. The architecture has three main parts. First, the encoder includes an adaptive local signal-to-noise ratio (SNR) module. This module filters multiplicative speckle dynamically. It stops the network from mistaking noise for real features. Second, the network bottleneck uses a lightweight Transformer block. This block learns global shape priors. It helps the network find the continuous coastline even when radar shadows cover parts of the image. Third, the decoder uses an adaptive edge refinement mechanism. A composite edge-aware loss guides this mechanism. It fixes rough edges and corrects pixel boundaries. This step removes jagged artifacts. Tests on a near-shore dataset show the reliability of this method. EAU-Net achieves a Dice Similarity Coefficient (DSC) of 0.9601 and an Intersection over Union (IoU) of 0.9234. The Average Symmetric Surface Distance (ASSD) is 5.7501 pixels. This paper compares EAU-Net with classical convolutional networks, global-attention architectures, and vision foundation models. The results show that EAU-Net preserves boundary precision better. It outperforms state-space models like VM-UNet and large-scale zero-shot models like SAM-Med2D. EAU-Net uses physical inductive biases to separate coastal building interference from marine targets. This provides a realistic foundation for downstream tasks like autonomous ship detection.
Bai, YuanJie, Zhong, Sheng
A Multi-Platform Bearing-Only Localization Method Based on Confidence Interval and Error Compression Iteration2026-99-294610/19/2026
Due to the fact that passive underwater acoustic signals can usually only obtain the bearing information of the target and are subject to complex ocean environmental interference, which poses significant obstacles to target localization using a single platform, this study intends to employ multiple unmanned underwater vehicles for cooperative localization of underwater targets. To address the issues of systematic bias in bearing measurements, high nonlinearity, and unknown target motion states in complex underwater acoustic environments, this paper proposes a bearing-only localization method based on confidence interval and error compression iteration. The unknown motion process of the target is mapped into a region expansion process in space, and the cooperative localization of multiple platforms is used to finely compress the regional error, achieving bearing-only localization of underwater targets through gradual region compression. The core idea of the proposed method is to map the unknown motion state of the target into an expansion process of an uncertainty region. First, bearing-only measurements obtained by multiple platforms at different time instants are used to construct sector regions with systematic bias. Then, a likelihood function is employed to update the probability distribution over the spatial grid, enabling iterative error compression. Meanwhile, the 95% highest posterior density interval is introduced as the statistical output of the localization results. Simulation experiments were conducted under three different initial scenarios. The results show that, with bounded bearing measurement errors, the proposed method can compress the area of the 95% HPD region from approximately 107 m2 to the order of 104 m2 within 120 seconds, while the localization error converges from several kilometers to less than 40 meters.
Huang, Fengfeng, Wang, Yuxuan, She, Tao
Magnetometer Temperature Compensation of Geomagnetic Directional Technology2026-99-295710/19/2026
Accurate compensation of magnetic measurement errors is one of the key factors in ensuring the accuracy of geomagnetic navigation. Magnetic sensors are susceptible to temperature drift during use due to the disturbance of external ambient temperature, which affects the accuracy of magnetic field measurement. To improve the precision of the tri-axis geomagnetic sensor, a suitable thermal error compensation algorithm must be designed to process measured data correctly. Thus, a temperature compensation model and a high-precision geomagnetic directional technology were proposed in this paper. Firstly, a tri-axis geomagnetic sensor temperature compensation model based on particle swarm optimization-least squares support vector machines (PSO-LSSVM) was established. Then the inertial measurement unit (IMU) data is processed with zero drift correction and filtering. Finally, geomagnetic directional experiments were conducted with the gradient descent method. The experimental results show that the proposed temperature error compensation method can effectively alleviate the impact of thermal variations on the detector output and enhance the precision of the geomagnetic orientation system. It is expected to be applied to multiple navigations, aviation, and aerospace fields.
Fan, Chenyu, Guan, Feng, Liang, Zhuo, Zhang, Haifeng
PPO-Based HAP Deployment for Throughput Maximization in SAGIN2026-99-292810/19/2026
Space-Air-Ground Integrated Networks (SAGINs) have attracted extensive attention as an effective solution for providing worldwide communication services and supporting diverse transmission requirements. Nevertheless, the highly dynamic wireless environment and fluctuating user distribution introduce considerable difficulties for efficient deployment of High-Altitude Platforms (HAPs). This work studies a throughput enhancement problem in a SAGIN architecture composed of a Low Earth Orbit (LEO) satellite, multiple HAPs, and terrestrial users. Considering practical communication constraints such as beam scheduling and subchannel assignment, the objective is to improve the overall data transmission capability through optimized HAP positioning. By interacting with varying network states, the proposed learning framework agent continuously learns suitable deployment policies for HAPs, thereby improving transmission performance and spectrum utilization efficiency. Numerical evaluations indicate that the policy optimization strategy achieves superior throughput performance and communication efficiency compared with traditional fixed deployment approaches.
Wang, Yin, Zhou, Keyan, Shao, Shuai, Zhang, Bin, Chen, Peifu, Wang, Jianfeng
PI-Unet Rapid Prediction Method for Satellite Transient Temperature Fields Based on Filtering Concept2026-99-294210/19/2026
To address the low computational efficiency problem by numerical calculation and large cumulative error by long-term sequential iterative prediction during the process of predicting the satellite’s transient temperature fields, this paper proposes a Physics-Informed U-Net (PI-Unet) model. Specifically, this PI-Unet model uses multi-branch spatiotemporal features as its inputs and combines hard constraints with a physics-informed loss to ensure the physical consistency of the predicted temperature field. Furthermore, for the error accumulation problem in long-sequence autoregressive prediction models, this paper presents a model training method based on the concept of filtering. Under the guidance of this method, the PI-Unet model can develop a long-term sequence prediction capability while keeping the accuracy of spatial temperature field prediction, achieving high-precision spatiotemporal prediction. By actively injecting prediction errors through a scheduled sampling strategy, along with a multi-step rolling training strategy, the PI-Unet model not only learns the governing physical laws of the spatial distribution of the transient temperature field, but also acquires the filtering capability to mitigate the accumulation of the long-term sequence rolling prediction errors. The experimental results indicate that the PI-Unet model maintains the maximum absolute error of predicting spatial transient temperature field stably below 5.5K and the error of the rolling-predicted transient temperature field in one orbit period less than 6K. In addition, the time for a single-step prediction is reduced to 0.005s, and the time for a total-period prediction is less than 0.5s. Therefore, this research provides a strong and high-efficiency technical support for the real-time monitoring of the satellite’s on-orbit temperature fields.
Li, Wanze, Xiao, Fuhong, Xue, Xiaoguang
Research on Multi-Source Navigation Information Fusion Based on Adaptive Weighted Least Squares Algorithm Considering Signal Quality and Geometric Distribution2026-99-296010/19/2026
With the extensive application of satellite navigation technology in intelligent transportation and aerospace fields, the fusion of multi-source navigation information has become a key approach to solving the problem of high-precision positioning in complex environments. To address the issues of single weighting and insufficient robustness in traditional weighted least squares (WLS) algorithms for multi-source navigation information fusion in complex environments, this paper proposes an adaptive weighted least squares (AQG-WLS) algorithm based on signal quality and geometric distribution. This algorithm dynamically adjusts the weights of observations by constructing a composite weight model that integrates the carrier-to-noise ratio and the spherical geometric distribution of satellites. On the one hand, it uses the carrier-to-noise ratio to reflect the level of observation noise corresponding to signal strength. On the other hand, it introduces the area of spherical quadrilaterals to quantify the correlation of satellite geometric configuration, thereby avoiding the deterioration of the geometric dilution of precision (GDOP) caused by satellite clustering. Experimental results show that in scenarios with obstructions such as urban canyons, the AQG-WLS algorithm improves positioning accuracy by approximately 23% compared to the traditional WLS algorithm, and demonstrates stronger robustness against multi path effects and gross errors, providing an effective parameter estimation method for multi-source navigation fusion.
Zhu, Shengling
Fault Diagnosis Study of MCSECNN Model Integrating MK-MMD and DANN under Variable Operating Conditions2026-99-297210/19/2026
Bearing signals measured under different loads rarely keep the same waveform characteristics. Their amplitudes, spectral components, and statistical properties may change at the same time, which makes a classifier trained on one load condition less reliable on another. This study investigates a cross-load diagnosis method that trains a multi-scale convolutional squeeze-excitation network (MCSECNN) with multi-kernel maximum mean discrepancy (MK-MMD) and a domain-adversarial neural network (DANN). The convolutional branch captures vibration patterns over several temporal ranges, and the SE unit adjusts the importance of the channels generated by the parallel kernels. MK-MMD penalizes the distance between load-dependent feature distributions, whereas DANN weakens the load identity retained in the features. Tests using CWRU bearing data show that the integrated model gives better accuracy than the basic MCSECNN and the two single-adaptation variants. Ablation results indicate that the statistical and adversarial constraints contribute to transfer performance in different ways.
Ming, Mei, Ai, Yumei, Li, Shengnan, Wan, Haozheng, Sang, Shentian, Zhang, Haifeng
An Adaptive Cloud Prediction Network for Supporting Space-Ground Laser Communication Links2026-99-298210/19/2026
Space-ground laser communication links are highly susceptible to interruptions caused by cloud cover. Reliable dynamic scheduling of ground stations and seamless link handover, therefore, requires accurate short-term cloud-layer forecasts. However, cloud evolution is inherently chaotic, and atmospheric conditions exhibit evident seasonal non-stationarity. These factors weaken the prediction stability of existing models and make it difficult to maintain consistent accuracy over extended forecasting horizons. To address these challenges, this paper proposes KFMNet, an adaptive cloud prediction network that integrates Koopman theory with frequency-domain Mamba modeling. Unlike conventional frameworks that mainly process cloud features in the spatial domain, KFMNet embeds a linear state-space modeling mechanism into the frequency-domain feature evolution process. Specifically, an adaptive spectral gating mechanism is used to capture global cloud distribution patterns in the frequency domain, thereby reducing the computational burden associated with high-resolution image sequences. Meanwhile, a Koopman seasonal gating module introduces monthly prior knowledge into the network, enabling the model to adapt to atmospheric drift across seasonal transitions. Within the encoder-decoder backbone, KFMNet jointly incorporates temporal modeling, frequency-domain representation, and physical consistency constraints, providing a more stable framework for short-term cloud prediction. We evaluate KFMNet on multi-season cloud images collected over Nanjing and Beijing. The climate profiles of these two cities are markedly different. Under these conditions, KFMNet achieves competitive accuracy in cloud segmentation and shows a better ability to handle seasonal and regional shifts than several reference models. In essence, our method adapts well to changing atmospheric conditions and provides a new cloud prediction approach for space-ground laser link assurance. This method combines physical interpretability with the actual needs of real-world deployment.
Wu, Xibing, Meng, Xiangxu, Zhong, Yun, Li, Wenfeng, Zhao, Kanglian
Distributed Cooperative Task Allocation for Many-to-Few On-Orbit Servicing Missions2026-99-293010/19/2026
This paper investigates cooperative task allocation for multi-satellite clusters under onboard functional payload constraints in many-to-few low Earth orbit non-cooperative servicing scenarios involving heterogeneous space targets. Although the servicing satellite cluster has a numerical advantage, the effectiveness of a single satellite is limited by orbital maneuver cost and the stringent requirements of onboard functional payload systems on rendezvous accuracy and interaction timing. As a result, multiple satellites are generally required to cooperate to establish local numerical superiority for effective task execution. Meanwhile, differences in object strategic importance and resistance capability may lead to excessive resource concentration on high-value objects, thereby degrading overall operational efficiency. To address these issues, an existing multi-satellite cooperative task planning framework is adopted to characterize the matching relationship between resource supply and task demand by jointly considering maneuver cost, target value heterogeneity, and coalition-size requirements. On this basis, a distributed cooperative mechanism is introduced to enable the satellite cluster to adaptively form coordinated task coalitions matched to object value. Simulation results show that, in representative many-to-few LEO servicing scenarios, the resulting allocation scheme can establish local cooperative resource concentration while satisfying full target coverage and no-idle-satellite constraints. It also suppresses excessive competition for high-value objects and improves the balance of resource utilization, thereby demonstrating the effectiveness of the proposed approach for cooperative task allocation in multi-satellite LEO servicing scenarios.
Liu, Jiawei
A Hybrid Routing and Domain-Boundary Authentication Framework for Heterogeneous Multi-Operator GEO-LEO Satellite Networks2026-99-295510/19/2026
Heterogeneous multi-operator GEO-LEO satellite networks serve as key infrastructure for next-generation global broadband communications. However, existing routing and security frameworks fail to resolve two fundamental challenges. First, orbital layers display inherent topological asymmetry: LEO intra-domain topologies suit fixed routing, while GEO-LEO links change dynamically. This dynamism makes pre-planned SR paths across the GEO layer unfeasible. Second, multi-operator networks introduce cross-domain trust boundaries without pre-established PKI. Centralized packet authentication is impractical for large constellations with intermittent ground contact. Existing solutions either use uniform SR or ground-assisted authentication, neither maintaining correctness in unstable polar regions. This paper proposes H-SID, a hybrid routing and authentication framework for multi-operator GEO-LEO networks. H-SID adopts a dual-layer architecture: SRv6 uSID enables deterministic routing in LEO domains, and a lightweight dynamic protocol handles real-time GEO forwarding. We design uSec, a 16-bit uSID instruction with VER and AuthType fields, allowing GEO nodes to adjust forwarding independently in polar instability. BG nodes perform SR-to-IP conversion and IBC-based authentication for cross-domain flows. GEO satellites act as offline IBE PKGs, issuing long-lived keys to BG nodes without data-plane operations. H-SID concentrates overhead at boundaries and works without cross-operator PKI, ensuring deployability.
Gao, Yuan, Li, Congcong, Ma, Hongxiao, Zhang, Shuo, Yan, Jingshan, Liu, Huiyong
Trust-Aware Deep Reinforcement Learning for Access Control in Satellite Communication Systems2026-99-293610/19/2026
The rapid evolution of satellite communication systems enables direct network access for terrestrial users via portable devices, but also introduces critical security challenges in access control. In particular, identity hijacking attacks allow malicious users to exploit legitimate access channels, leading to inefficient occupation of scarce satellite resources. Existing approaches mainly depend on trust evaluation or heuristic-based decision strategies, which are inadequate for adapting to dynamic environments. To address this issue, this paper proposes a trust-aware deep reinforcement learning (DRL) framework for satellite access control, which jointly considers user behavior evaluation and decision optimization. Specifically, a Bayesian trust evaluation model is first developed to quantify the credibility of user behavior by incorporating historical interaction records as prior information. Unlike conventional static trust schemes, an adaptive mechanism is introduced to enhance sensitivity to behavioral dynamics. Building upon the trust assessment, the satellite access process is formulated as a Markov Decision Process (MDP), where system states integrate channel conditions, user trust levels, and service demands. A Deep Q-Network (DQN) is then employed to learn optimal admission and scheduling policies that maximize long-term system utility under resource constraints. Simulation results demonstrate that the proposed method significantly outperforms conventional greedy and threshold-based strategies. In particular, it effectively mitigates the impact of malicious behaviors and improves system utility by over 80%, while achieving more efficient resource utilization in dynamic environments.
Zhou, Qihao, Liu, Wei, Zhang, Hongyuan, Chen, Te, Wang, Yuqi
Lightweight Compression Networks for Efficient RGBD-IR Imagery Transmission on Resource-Constrained Edge UAV Platforms2026-99-297810/19/2026
Due to the limitation of unstable wireless links and resource-constrained edge computing device, it is challenging to transmit the unmanned aerial vehicles (UAVs) captured images with large data volume in real-time. To address this issue, this paper proposes a lightweight hybrid compression method named Jpeg Plus Plus Network (JppNet) for efficient multimodal image transmission. Different from existing methods that mainly focus on RGB image compression for offline storage and often use large networks for coding and reconstruction, the proposed JppNet focuses more on edge deployment under limited computing resources. At the encoder side, JPEG is first used to perform high-ratio compression on the RGB, D, and IR channels. Then, a lightweight convolutional encoder network extracts supplementary binary features for progressive transmission. At the decoder side, a medium-scale reconstruction network fuses the supplementary code with the JPEG reconstruction to restore image details. To verify the effectiveness of the proposed method, this paper built a multimodal data acquisition prototype and collected 20,000 high-quality RGBD-IR image samples for training and testing. Experimental results show that JppNet combines the high coding efficiency of JPEG with the detail recovery ability of convolutional neural networks. With low computational cost, JppNet obtains much lower reconstruction errors than JPEG on the RGB, D, and IR channels, especially at very low compression ratios. This supports the efficient transmission of UAV captured multimodal images under limited communication conditions.
Pi, Haoran, Ao, Yile, Li, Zhi, Tang, Gang, Jiang, Bowu
Methodological Study on Fluid-Structure Interaction Dynamics of Spacecraft in Martian Rarefied Atmosphere Aerobraking2026-99-293210/19/2026
During aerobraking, spacecraft deploy large flexible solar arrays to obtain enough aerodynamic drag. In the rarefied Martian atmosphere, rapidly varying dynamic pressure causes low-frequency elastic vibrations of these appendages, leading to coupled translation-rotation-vibration fluid-structure interaction (FSI) that affects both aerodynamic characteristics and attitude stability. Traditional high precision methods such as DSMC are difficult to apply to long-duration simulations owing to their high computational cost. To solve this problem, this paper proposes a one-way FSI framework, which conbines a cross-regime panel method for aerodynamic calculation with a reduced-order multibody dynamic model. The simulation results show that the framework can reduce the computational burden while retaining the main physical characteristics required for the analysis. The aeroelastic response of a representative spacecraft configuration is analyzed, and the results can support configuration design, aerobraking corridor constraints, and control system development.
Zhou, Tianhao, Cui, Hutao
Multi-Objective Optimization Design Method for MEO Constellations Oriented towards LEO Constellation Coverage2026-99-293110/19/2026
Aiming at the challenges faced by Low Earth Orbit (LEO) constellations, such as difficulties in cross-shell communication, complexity in global management and control, and highly dynamic network topologies, this paper evaluates the introduction of a Medium Earth Orbit (MEO) constellation as a management layer to optimize the coordination and servicing of the underlying LEO constellation. The core contributions of this work are twofold. First, a coverage performance analysis method based on Voronoi diagrams and Delaunay triangulation is adopted to evaluate the coverage of MEO constellations over LEO constellations. Second, a four-dimensional multi-objective fitness function is constructed, comprehensively considering launch cost, communication delay, coverage performance, and space security. On this basis, a multi-objective genetic algorithm is employed to optimize the key parameters of a Walker-type MEO constellation, including orbital altitude, orbital plane count, and satellites per plane. Simulation results show that, under the condition of a LEO constellation altitude of 600 km, highly fit MEO configuration parameters are obtained. STK simulations verify that this configuration can achieve complete and dynamic coverage of the underlying LEO constellation. This study provides a systematic theoretical framework and an efficient algorithmic tool for solving the configuration optimization problem of the MEO layer in the "LEO-MEO" architecture.
Li, Ruiyao, Cui, Hutao
Research on Temperature Hysteresis Error Suppression Technology for Capacitive MEMS Accelerometers2026-99-296810/19/2026
The performance of MEMS accelerometers is highly sensitive to temperature variations, manifesting as zero drift, sensitivity drift, and hysteresis error. This paper presents a low-temperature-drift, low-noise interface ASIC for capacitive MEMS accelerometers. The circuit employs an analog closed-loop architecture comprising a small-capacitance front-end detection circuit, a variable gain amplifier (VGA), a correlated double sampling (CDS) circuit, a PID controller, and a feedback module. To suppress temperature-induced errors, key optimizations are implemented: the charge amplifier employs active and passive charge compensation techniques to reduce common-mode offset drift; the VGA uses switched feedback resistors for gain adjustment; a Class-AB unity-gain buffer enhances driving capability while isolating load interference; and a high-order compensated bandgap reference with pre-regulation achieves low temperature drift and high power supply rejection. Post-layout simulations demonstrate a system sensitivity of approximately 2.27 V/g, a sensitivity temperature coefficient of 74.55 ppm/°C, and a 1σ zero-bias temperature drift of 1.757 mg. Experimental results over the full temperature range from -45°C to 85°C show an output hysteresis error of only 0.6 mg. These results validate the effectiveness of the proposed temperature hysteresis error suppression technique, providing a valuable reference for the design of high-precision MEMS accelerometer interface circuits.
Zhao, Weiqiao, Liu, Guowen, Hao, Zhiwen, Li, Zhaohan, Gao, Naikun, Zhang, Wenbo, Fu, Qiang, Yin, Liang
Research on Nonlinearity of Interface ASIC for MEMS Accelerometer with Switched-Capacitor Closed-Loop Feedback2026-99-296910/19/2026
This paper investigates the nonlinearity issues in the interface ASIC system for capacitive MEMS accelerometers. First, the sources of nonlinearity in the mechanical structure of the accelerometer and the switched-capacitor interface circuit are analyzed, including the nonlinearity of mechanical displacement-to-capacitance conversion, harmonic distortion caused by parasitic capacitance, and the contradiction between range and linearity due to a low-stiffness proof mass in open-loop mode. To suppress the nonlinearity, PID control is adopted to realize closed-loop electrostatic force feedback, a loop error model is established, and a capacitor array compensation circuit is introduced to eliminate the output offset caused by parasitic capacitance. The interface ASIC is designed in a 0.18 μm BCD process, and the layout design and tape-out are completed. Pre-simulation results show a nonlinearity of 0.0124% within a 0–50 g range; post-simulation nonlinearity is 0.0657%. In actual tests, the accelerometer achieves a scale factor of 0.091 V/g over the 0–50 g range, the maximum residual of the least-squares fitting is 0.000645 V, and the nonlinearity is 0.0728%, approximately 160 ppm. This study verifies the effectiveness of closed-loop PID control and capacitance compensation in suppressing nonlinearity, providing a theoretical basis and engineering reference for the design of high-linearity MEMS accelerometers.
Gao, Zhi, Li, Zhaohan, Hao, Zhiwen, Liu, Guowen, Gao, Naikun, Zhang, Wenbo, Fu, Qiang, Yin, Liang
Adaptive Parameter Optimization for BP/LTP in Deep Space Communication Based on a Deep Q-Network2026-99-299610/19/2026
This study addresses the limited adaptability of Bundle Protocol/Licklider Transmission Protocol (BP/LTP) parameter configuration in deep-space communication environments through a cooperative optimization mechanism that operates under dynamic link conditions and diverse service demands. Conventional BP/LTP parameter settings typically rely on fixed thresholds or empirical rules, assuming relatively stable link states. However, in practical deep-space missions, the bit error rate, propagation delay, and available transmission rate all exhibit significant temporal variability. Moreover, variations in data size and reliability requirements further exacerbate the performance degradation caused by parameter mismatches. Owing to the multi-layer encapsulation and resource coupling of bundle size, block size, segment size, and the number of sessions, inappropriate parameter configurations can result in increased acknowledgment and retransmission overhead, underutilized link capacity, and prolonged end-to-end delivery latency. Traditional approaches are thus inadequate for achieving holistic, real-time, and joint optimization in such dynamic environments, necessitating adaptive parameter tuning mechanisms with link-state awareness. This paper formulates BP/LTP parameter optimization as a sequential decision process and proposes a method based on a Deep Q-Network (DQN) for adaptive parameter configuration. The state space includes transmitted data size, bit error rate, one-way propagation delay, and available transmission rate. Based on this state information, the DQN selects the bundle size and LTP block size from predefined candidate sets. The LTP segment size is derived from a mapping rule related to link conditions, and the number of concurrent sessions is calculated online using an analytical model. The method is evaluated on a satellite–ground simulation platform that consists of a BP/LTP sender, a BP/LTP receiver, and a computing unit for DQN parameter optimization. It is compared with three fixed parameter configurations in 36 experimental scenarios. The results show that, compared with the fixed configurations, the proposed method reduces end-to-end delivery latency, improves link utilization efficiency, and maintains more stable transmission performance under different link conditions and data scales.
Han, Yingxiang, Bian, Mingyue, Chang, Xiang, Cao, Suzhi, Yan, Lei
Trajectory Planning for Heterogeneous Multi-Satellite Cooperative Proximity Operations2026-99-291410/19/2026
This study addresses the challenges in heterogeneous multi-satellite cooperative proximity operations, including functional heterogeneity, strong temporal coupling, and difficulties in unified planning. A functionally layered framework composed of observation, support, and servicing satellites is constructed. Considering the distinct mission characteristics of different satellite types, differentiated decision variables, objective functions, and constraints are designed, and a particle swarm optimization algorithm is employed for solution. Simulation results demonstrate that the proposed method can generate feasible cooperative trajectories satisfying constraints such as safety distance, temporal coordination, communication links, and mission windows, while maintaining system stability and operational safety. Parametric analysis indicates that mission duration, minimum access distance, and support effectiveness significantly affect system performance. Through functional decomposition and cooperative optimization, the system achieves an effective trade-off among different preferences, providing theoretical support for the mission design and control of heterogeneous multi-satellite proximity operations.
Xiao, Jinzhao, Zhang, Jin
Analysis of MIMO Channel Characteristics under Large Spatio-Temporal Scales Based on Distribution Formula2026-99-296210/19/2026
In recent years, the global demand for high-rate, high-reliability, and wide-coverage wireless communications has continued to grow. Traditional satellite communications are faced with bottlenecks such as spectrum scarcity, insufficient single-satellite capacity, and poor adaptability to highly dynamic channels. Relying on the advantages of wide-area coverage and multi-satellite cooperation, low Earth orbit (LEO) satellite constellations can construct distributed satellite MIMO systems and form virtual large-scale antenna arrays, significantly improving system capacity and reliability. However, such systems are characterized by large spatiotemporal scales, and their channels exhibit strong heterogeneity in dimensions such as delay, frequency, and polarization, which are difficult to accurately evaluate using existing methods. To this end, this paper proposes a large-spatiotemporal-scale channel characteristic analysis method for distributed LEO satellite MIMO systems. A satellite-terrestrial full-link channel model is established, and quantitative characterization of multi-dimensional heterogeneity is realized based on statistical boundary analysis. Combined with the H3 global grid system, large-scale simulations are carried out to statistically analyze the distribution laws of delay differences, frequency offsets, and polarization angle deviations under different user configurations. The unique value of this analysis method lies in that its output results can be used to determine the upper limit of buffer capacity required for the receiver to complete coherent combining of multi-channel signals and the frequency offset correction range required for signal acquisition, provide a quantitative basis for optimizing satellite set selection, and offer a solid theoretical model and data support for performance evaluation and parameter design of multi-satellite cooperative systems.
Lu, Yi, Hou, Yucheng, Su, Xingyu, Yu, Xinyue, Jiang, Yanzi, Zhang, Jie, He, Wenxi
Move Blocking Event Triggered Model Predictive Control of Spacecraft Formation2026-99-295610/19/2026
This paper addresses the problem of formation reconfiguration for deputy spacecraft flying in a spatial circular formation around a chief spacecraft. To improve the real-time applicability of formation control under onboard computational limitations, a reconfiguration method is proposed by integrating Tschauner–Hempel (T-H) relative dynamics, Event-Triggered Model Predictive Control (ET-MPC), and a Move Blocking strategy, while explicitly considering collision-avoidance constraints. Based on the T-H relative dynamic model, an MPC framework is established to generate control inputs for the reconfiguration process. To reduce the computational burden of conventional MPC, an event-triggering mechanism is introduced so that the optimization problem is solved only when necessary, rather than at every sampling instant. Meanwhile, a Move Blocking strategy is employed to reduce the number of online decision variables by constraining successive control moves over predefined intervals. In addition, to simultaneously address inter-spacecraft collision avoidance and limited-thrust constraints, an optimization model with binary variables is formulated. To evaluate the proposed approach, four simulation cases, namely MPC, ET-MPC, MB-MPC, and MBET-MPC, are conducted under the same reconfiguration scenario. Simulation results show that all four methods can successfully accomplish the formation reconfiguration task while satisfying the minimum safe-distance requirement. Moreover, the methods incorporating Move Blocking reduce the computation time required for solving each individual optimization problem, while the event-triggered schemes significantly reduce the number of optimization updates compared with the corresponding time-triggered methods, thereby further lowering the overall computational cost. The results demonstrate that the proposed method provides an effective and feasible solution for real-time formation reconfiguration and control on resource-constrained spacecraft platforms.
Zhang, Yuning, Zhang, Zexu, Liang, Jintang
Rapid Trajectory Optimization for Mars Aerobraking Campaigns2026-99-294010/19/2026
Aerobraking is a key technology for reducing propellant consumption in deep space exploration missions. Targeting the Mars aerobraking trajectory optimization problem, this paper proposes a rapid trajectory optimization method based on a high-precision analytical approximate solution. First, a Mars aerobraking dynamics model is established. Based on the assumptions of a linear flight path angle and a linear altitude rate, a high-precision analytical approximate solution for Mars aerobraking trajectories is derived. Simulation results verify that the proposed analytical solution satisfies the accuracy requirements for Mars aerobraking trajectory optimization. Based on this analytical solution, the state transition function of the aerobraking trajectory optimization problem is reconstructed, and the Sequential Quadratic Programming (SQP) algorithm is employed for the optimization. Numerical simulation results demonstrate that the proposed method requires less than one minute to optimize an aerobraking campaign consisting of 80 atmospheric passes. Compared with traditional optimization methods, it exhibits significant advantages in computational efficiency when dealing with aerobraking trajectory optimization problems characterized by long durations and multiple velocity pulses.
Ding, Yuancheng, Dong, Jie, Cui, Hutao, Chen, Zheng
OpenClaw-Enabled Real-Time Diagnosis of Inter-Satellite Links in Decentralized Satellite Network Emulation2026-99-291510/19/2026
With the continued expansion of low Earth orbit (LEO) satellite constellations, inter-satellite links (ISLs) have become a critical infrastructure for network connectivity and end-to-end forwarding capability. Existing studies often show a gap between the physical layer and the system layer: physical factors such as link budget, pointing error, and Doppler mismatch are frequently simplified, while link performance analysis is difficult to validate in a closed loop using a real protocol stack. In addition, dynamic topology and time-varying link states make fault detection and localization highly complex.To address these problems, this paper constructs a decentralized satellite network emulation platform and proposes a decentralized addressing and connectivity verification method for full inter-satellite link interfaces. At the physical layer, an optical inter-satellite link (OISL) model is developed by incorporating free-space path loss, Doppler mismatch, and BER-FEC-PLR mapping, with its dynamic evolution driven by orbital parameters. At the system layer, based on a one-satellite-one-container architecture, OpenClaw is introduced to perform link detection and log analysis, thereby generating diagnostic reports.Experimental results show that the proposed method supports interface-level link detection, identifies dynamic link faults such as transient impairments caused by Doppler mismatch in real time, and generates corresponding diagnostic reports.
Zhao, Jiangtong, Luo, Jiangtao, Zhang, Jinshuo, Ran, Yongyi
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