Browse Topic: Electrical, Electronics, and Avionics

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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
Deep Reinforcement Learning for Downlink User Scheduling in LEO Satellite Massive MIMO Systems2026-99-299010/19/2026
Low earth orbit (LEO) satellite communication system is expected to play a key role in the 6G wireless networks. Due to the limited communication resource and inter-user interference (IUI), the user equipments (UEs) must be well scheduled to maximize spectral efficiency. However, rapid channel variations and high mobility in LEO system make the conventional user scheduling strategies relying on instantaneous channel state information impractical. To address this challenge, this paper proposes a deep reinforcement learning (DRL) framework based on batch normalization in CrossQ algorithm to optimize the downlink user scheduling in a LEO satellite massive multi-input multi-output (MIMO) system. The scheduling task is modeled as a Markov decision process (MDP), where the observation space integrates high-dimensional features including large-scale fading (LSF) coefficients, channel matrix, and temporal features capturing the orbital progress of the satellite. Unlike traditional DRL methods, the CrossQ-based algorithm applies batch normalization (BN) within the critic networks to eliminate the need for slowly-moving target networks, fostering training stability and sample efficiency. A continuous priority score vector is mapped to discrete scheduling decisions through a top-z selection logic, allowing the agent to effectively identify spatially orthogonal user subsets to suppress the IUI. To guide the agent toward the true optimization direction, a differential reward mechanism is designed. Simulation results demonstrate that the proposed CrossQ-based scheduler consistently achieves nearly optimal performance at different signal-to-noise ratios (SNR), reaching at least 91% and 94% of the Monte Carlo search ceiling under different user densities. Moreover, the trained model provides scheduling strategy in millisecond inference time, making it a practical and robust solution for future 6G non-terrestrial networks.
Deng, Chaojun, Zhong, Yun, Meng, Xiangxu, Li, Wenfeng, Zhao, Kanglian
Emergence and Regulation of Scale-Free Correlation in UAV Swarm under Communication Energy Constraints2026-99-298310/19/2026
This paper studies how scale-free correlations, a key feature for efficient group response, emerge in collective motion under communication energy constraints. A two-dimensional Boids model integrated with a point-to-point wireless communication energy consumption model is used. The model dynamically determines, based on a limited communication power budget, an individual’s interactive neighbors, where neighbors are selected based on two different mechanisms: distance-based and probability-based, until the total communication cost reaches the budget. Simulations of flock sizes ranging from 30 to 230 individuals confirm that scale-free correlation persists, even under strict power constraints, which is shown by a correlation length ξ proportional to the flock’s linear size L. Notably, the ratio ξ/L, which reflects the robustness of collective correlation, displays a non-monotonic dependence on the available communication power. Initially, increasing power enhances ξ/L by expanding effective interaction ranges and improving information transmission. However, beyond a threshold, further power increase causes a slow drop in ξ/L, likely due to signal interference from excessively many or distant connections. This finding demonstrates that communication power is a critical, tunable engineering parameter regulating collective coordination, analogous to interaction range or neighbor number in biological models. The results imply that for engineered swarms, maximizing communication resources is not always beneficial; instead, an optimal power allocation exists to maximize coordination performance, balancing connectivity with efficient, localized interactions.
Zhao, Shiping, Gan, Qingzhong, Liu, Jinglei, Liu, Zhihao, Zhang, Liang
LASERAgent: Toward Highly Automated and Reliable Reproduction of LEO Networking Experiments2026-99-299110/19/2026
It is still difficult to reproduce the low-Earth-orbit (LEO) networking experiments since the experimental settings are scattered and the Large Language Model (LLM) assisted workflow is laborious and needs to be refined by manually crafting the prompt, transferring the code and debugging. Existing retrieval-assisted pipelines still demand substantial human effort for parameter inference and code repair, particularly given the precise alignment required for LEO topology, constellation, and simulator configurations. This paper presents LASERAgent, a highly automated agent system for LEO networking experiment reproduction. Unlike retrieval-assisted pipelines that mainly support evidence retrieval and code generation, LASERAgent organizes the downstream workflow into a bounded pipeline of retrieve - extract - generate - check - execute, integrating structured parameter extraction, template-guided generation, deterministic validation, controlled execution, and limited retry. Preliminary results show that LASERAgent reduces required human interventions by roughly half while improving result consistency over prior retrieval-assisted workflows, and that the validation stage plays an important role in reliable automation.
Tian, Siyu, Wei, Guangyu, He, Xiongwen, Liu, Donghua, Ma, Yue, Zhan, Yuheng, Xia, Lang
A Deep Learning Approach for Space Target Pose Estimation Based on Multi-Task Joint Training2026-99-298110/19/2026
The growing number of space objects raises collision risks and demand for on-orbit maintenance. Since most servicing tasks require observing the target, relative pose estimation is key for debris removal and satellite servicing. The complex orbital environment poses significant challenges for conventional feature extraction networks.. To address these challenges, this paper presents a multi-task convolutional neural network for space target pose estimation. The network uses a MobileNetV3-based encoder to extract image features and an Adaptively Spatial Feature Fusion (ASFF) module for adaptive multi-scale feature fusion. Although MobileNetV3 and ASFF have rarely been combined in the aerospace field, their combination offers significant advantages in low parameter count and reduced computational load, showing strong potential for spacecraft pose estimation. Two parallel prediction heads are employed: an EfficientPose head consisting of classification, bounding box regression, rotation matrix regression, and translation vector regression, together with a Heatmap head. This design leads to more robust and accurate pose estimation. The loss function follows a multi-prediction-head joint approach, where each component is weighted to form the final loss for multi-task optimization. The network is trained, validated, and tested on a simulation dataset of the DAWN satellite built with Unity. The dataset includes illumination variations, large distance changes, and multi-view target appearances, thus capturing the complex scenes typical of vision-based recognition tasks. Experimental results show that after 40 training epochs, the model achieves an average position error of about 0.05 m and a rotation error of about 0.8° on the test set. These results demonstrate the effectiveness and accuracy of the proposed network for space target pose estimation.
Wang, Wenzhi, Zhang, Zexu, Zhang, Fan, Song, Zhuo
Multivariate Satellite Telemetry Data Compression with Spatial Reduction Based on Linear Correlation-Guided Channel Selection and Receiver-Side Recovery2026-99-298910/19/2026
Satellite telemetry data compression is of great importance for preserving the integrity and effectiveness of telemetry information. As the number of telemetry parameters gradually increases, satellite states can be characterized from more comprehensive data dimensions. However, it is difficult to maintain long-term high-sampling-rate transmission for all channels constrained by telemetry bandwidth. This condition weakens the capability of telemetry data to capture satellite state variations in a sufficiently detailed manner. Therefore, it is still a challenge for selecting more representative telemetry channels and recovering the remaining dimensions. To address this issue, this paper proposes a data compression method based on linear correlation-guided channel selection and receiver-side Long Short-Term Memory Variational Autoencoder recovery, in which satellite telemetry data compression is achieved through spatial reduction. First, the Pearson correlation matrix of all channels is computed from telemetry data, and the structure among telemetry channels is analyzed based on a prior on pairwise linear relations. Second, a linear correlation-guided iterative channel selection strategy is developed under a fixed transmission budget. Candidate channels are scored by the residual correlation coverage matrix over the unselected channels, and the initially selected subset is further refined through local channel swapping to obtain a more globally representative transmitted subset. Finally, the transmitted channels are fed into the reconstruction model to recover the untransmitted channels, so as to ensure the transmission of telemetry information. Experiments are conducted on real multivariate satellite telemetry data under predefined transmission budgets. Results on 84-dimensional real operational satellite telemetry data show that the proposed method exhibits favorable data compression capabilities. Representative RMSE comparisons under different transmission budgets further showed favorable reconstruction performance, demonstrating the feasibility of the proposed method for telemetry data compression under limited communication resources.
Wang, Zhipeng, Zhang, Kuibin, Song, Yuchen, Peng, Yu
An Electromagnetic-Thermal-Mechanical Coupling Model of the Three-Coil Wear Debris Sensors for Wide-Temperature and Kinematic Analysis2026-99-298510/19/2026
Real-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
Shi, Xinfa, Duan, Chenhao, Zhen, Huanyi, Zhao, Yihe, Li, Min, Wang, Xiaozhang, Zhang, Qing, Li, Zhikang, Zhao, Libo
Research on Network Attack and Defense Technology in LEO Large-Scale Constellation Networking2026-99-298710/19/2026
With the rapid development of LEO large-scale constellations, space networks evolved into a critical domain of cyber confrontation. The heterogeneous architecture, open inter- satellite links and limited on-board processing capability introduce serious security risks. Aiming at advanced persistent threat (APT), data poisoning and spear phishing, this paper analyzes new attack mechanisms in LEO constellation networking, satellite-ground collaboration and communication - computation convergence scenarios. A satellite-ground-link collaborative active defense model is proposed to improve the security and stability of LEO constellation systems.
Guan, Mengmeng, Xia, Ming, Fang, Liang, Li, Ruxue, Lv, Xing
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
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
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
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
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
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
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
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
A Dynamic Primary/Backup Path Algorithm Based on Multi-Level Feedback Queue for Differentiated Services in LEO Satellite Networks2026-99-297710/19/2026
With the accelerated deployment of Low Earth Orbit (LEO) mega-constellations, such as Starlink and OneWeb, satellite networks are transitioning from providing best-effort connectivity to supporting stringent, mission-critical services, including autonomous driving coordination, ultra-high-definition video streaming, and real-time financial transactions. This paradigm shift imposes highly heterogeneous Quality of Service (QoS) requirements regarding latency, bandwidth, and reliability. However, traditional unified scheduling and static routing mechanisms are inadequate for handling the dynamic topologies and bursty traffic inherent in LEO environments, often leading to service starvation or excessive packet loss. To address these challenges, this paper proposes an integrated primary-backup path guarantee framework that tightly couples a Multi-Level Feedback Queue (MLFQ)-based dynamic scheduling algorithm with a Dynamic Weight Adaptive Routing Algorithm (DWARA). The MLFQ scheduler employs an aging-based priority promotion mechanism to dynamically adjust time slices, effectively preventing low-priority traffic starvation while ensuring ultra-low latency for Class A services. Concurrently, DWARA models QoS as a three-dimensional vector (Throughput, Delay, Reliability) and utilizes the eigenvector method to derive service-aware weights for path selection. To validate the efficacy of the proposed scheme, comprehensive simulations were conducted using STK and Python, emulating a 64-node polar constellation under varying fault conditions. The results demonstrate that the integrated framework significantly outperforms baseline algorithms. Specifically, it reduces the average blocking rate by up to 37% under scenarios with 10 faulty satellites compared to Dijkstra, while lowering end-to-end latency for delay-sensitive services to approximately 28 ms at a traffic arrival rate of 500. Furthermore, the framework achieves a 92% bandwidth satisfaction rate for high-throughput services and maintains near-zero packet loss for high-reliability transmissions, offering a scalable and robust QoS assurance solution for next-generation space-air-ground integrated networks.
Zhang, Hongyuan, Zhang, Pengfei, Zhou, Qihao, Gao, Zhao
Prediction of Tropospheric Scatter Transmission Loss Based on Meteorological Factors2026-99-297410/19/2026
Troposcatter communication is an important beyond-line-of-sight transmission technology for long-distance terrestrial links in infrastructure-limited environments. However, its transmission loss is strongly affected by meteorological conditions, and the resulting short-term fluctuations make accurate link-state prediction difficult. To address this issue, this paper investigates the prediction of troposcatter transmission loss based on meteorological factors and proposes an attention-enhanced prediction model for short-term forecasting. First, the physical relationship between atmospheric conditions and troposcatter transmission loss is analyzed. On this basis, an hourly dataset is constructed by combining meteorological parameters extracted from the ERA5 reanalysis dataset with the corresponding transmission-loss observations of a troposcatter link. The meteorological field is represented through spatial sampling along the link direction and vertical reconstruction across multiple altitude levels, so that both spatial structure and temporal evolution can be incorporated into the model input. The proposed model utilizes a convolutional neural network (CNN) for the extraction of spatial features from meteorological profiles and then uses a Transformer encoder to capture long-range temporal dependencies within the historical observation window. Simulation results suggest that the proposed model can track the time-varying pattern of transmission loss more accurately than benchmark models, including CNN and CNN-LSTM. The results demonstrate that the attention mechanism can effectively enhance the short-term prediction capability for troposcatter transmission loss and provide useful support for dynamic link assessment and operational decision-making.
Zhang, Kangkang, Zhao, Kanglian
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 Optimization of Fault Diagnosis Models Based on Reinforcement Learning and Robustness Quantification Feedback2026-99-299510/19/2026
Spacecraft operate in a highly complex in-orbit environment. Telemetry data from power systems is often plagued by multi-source disturbances such as plasma noise, electromagnetic interference, and communication packet loss, which places very strict demands on fault diagnosis model reliability. Using dynamic disturbance test manifolds and the comprehensive robustness quantification index (RQI) for evaluation, we found a critical vulnerability: baseline models such as the joint loss enhanced global sparse attention transformer (JLE-GSA-Transformer) perform well in resisting continuous signal disturbances, but their performance drops sharply when temporal structures are disrupted due to data missingness. This finding highlights that static diagnostic models have fragile robustness boundaries when operating under extreme non-stationary conditions. To solve this problem, this paper puts forward an adaptive optimization method that combines deep reinforcement learning (RL) with quantitative robustness feedback. By making use of the dynamic disturbance manifold, this method embeds the RQI into the reward function of the RL algorithm. Through continuous updates to the agent’s policy, it drives the adaptive adjustment of key parameters in the JLE-GSA-Transformer, mainly including the coding structure of global sparse attention (GSA) and the discriminative boundaries of temporal cosine-center joint loss. This closed-loop mechanism can significantly improve the stability, adaptability, and long-term reliability of the model when it works in complex environments with multi-source disturbances.
Liu, He, Wei, Cheng, Zhang, Zexu
Robust Cross-Modal UAV Detection via Bidirectional Collaborative Energy Field under Weak Calibration2026-99-299210/19/2026
The precise detection and localization of 'low-slow-small' (LSS) unmanned aerial vehicles (UAVs) pose significant challenges in terminal defense scenarios. These non-cooperative targets have extremely sparse representations in radar point clouds because of their minimal radar cross-sections and appear as textureless, blurred patches in visual images. Furthermore, traditional multi-modal fusion paradigms rely heavily on rigid geometric projection matrices, which frequently fail under the mechanical vibrations, thermal deformations, and extrinsic perturbations inherent in rapid deployment environments, which often results in severe feature misalignment and contamination. To overcome these critical bottlenecks, we present an end-to-end cross-modal detection framework built on Bidirectional Collaborative Energy Fields (Co-Field). By redefining the 3D object detection task as an energy field reconstruction process within a latent multimodal feature space, we entirely remove the need for absolutely precise extrinsic parameters. Our framework uses a novel bidirectional mutual-guided interaction mechanism. Specifically, a vision-guided radar branch employs deformable cross-attention to adaptively aggregate highly discrete radar geometric features around visual semantic centers, achieving a feature-level soft alignment that implicitly compensates for calibration drift. Concurrently, a radar-corrected vision branch utilizes radar-derived geometric occupancy probabilities as depth anchors to gate and calibrate visual features, effectively suppressing the inherent divergence of monocular depth estimation. Ultimately, the framework regresses the target's precise 3D states and fine-grained categories directly from the extrema of the reconstructed collaborative energy field, circumventing complex heuristic post-processing. Experiments on the curated MMAUD dataset show that the proposed Co-Field method achieves better robustness. It remains highly effective even under severe calibration errors up to 10 degrees and achieves a 74.2% recall rate at extreme sparse distances ranging from 70 to 100 meters, and consistently outperforms state-of-the-art rigid and soft fusion baselines.
Peng, Chao, Shao, Peng, Ding, Qiao, Chen, Xiaoqing, Han, Aojia, Xu, Yuelei
Study of On-Orbit Rigid-Flexible-Thermal Coupling Dynamic Response and Attitude Control of Large Spaceborne Deployable Parabolic Truss Antenna2026-99-290910/19/2026
Modern space exploration relies more and more on vehicles that carry expansive antenna arrays, a trend driven by steady progress in the field of space engineering. The study centers on constructing a coupled model that accounts for structural rigidity, flexibility, and thermal effects in a sizable orbital unfurling parabolic antenna, along with designing its orientation regulation system. A blended coordinate formulation and a heat-based discretization approach are employed to derive the motion and temperature-governing equations of the assembly. A unified pointing control method is then developed by merging classical feedback control principles with torque compensation control. Computational experiments involving a spacecraft fitted with such a dish antenna are executed. The findings reveal that sudden heat fluctuations, exemplified by the transition out of Earth’s umbra, trigger oscillatory behavior in the antenna framework and disrupt the satellite’s angular position. The designed orientation regulator successfully counteracts these thermally driven perturbations, even when the system representation contains inaccuracies.
Zhou, Shijie, Liu, Xiang, Cai, Guoping
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
Switched Adaptive Flight Control of UAV Carrier Subject to Transient Separation Disturbances2026-99-295210/19/2026
A switched adaptive flight control architecture is proposed for fixed-wing carrier-sub UAVs to handle abrupt mass and inertia variations, as well as parameter uncertainties induced by transient aerodynamic interference, during sequential deployment. The separation process is rigorously modeled as a switched linear system, and a combined direct and indirect adaptive control scheme featuring an online Recursive Least Squares (RLS) modification is designed to compensate for model uncertainties. Furthermore, a dwell-time admissible switching law is established by solving Linear Matrix Inequalities (LMIs), ensuring the stability of the switching process through theoretically guaranteed Lyapunov energy drops at switching instants. Numerical simulations verify that transient tracking error spikes are significantly suppressed, and high-frequency oscillations are effectively attenuated compared to baseline controllers.
Chi, Haiyi, Yuan, Shuai
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
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
Dual-Quaternion Based Nonlinear Model Predictive Control for Quadrotor Leader-Follower Formation2026-99-297110/19/2026
To effectively address the challenges of cooperative control for quadrotor formations in complex environments, this paper extends the dual-quaternion based nonlinear model predictive control (DQ-NMPC) framework to leader-follower formation. Unlike conventional methods that treat translation and rotation separately, the dual-quaternion based model achieves a unified and compact representation of rigid-body motion using only eight real numbers without singularities, enabling global description on SE(3). Through the logarithmic map, the pose error is mapped from the Lie group to its Lie algebra, resulting in a quadratic cost function that naturally resolves the dimensional inconsistency between position and attitude errors, thereby simplifying weight tuning and improving numerical stability. Building on this formulation, a UAV formation controller is developed, where the relative motion between leader and follower is concisely described using dual quaternions, and a minimum safe distance constraint is explicitly incorporated to avoid collisions and maintain safe separation. In formation scenarios, the follower autonomously accomplishes chasing, rendezvous, and formation keeping under various trajectories, including straight-line pursuit and Lissajous curves. Moreover, the stability of formation control is validated under non-ideal conditions such as communication delay, mass mismatches, and constant external disturbances. Furthermore, the optimal weight combination is determined through grid search, which further improves formation tracking accuracy and control smoothness. In addition, the proposed framework inherently supports time-varying formation patterns, such as dynamic switching between different relative configurations and adaptive scaling. Computational efficiency analysis shows that the average solution time per control cycle is far below the sampling interval, validating real-time feasibility. Overall, the DQ-NMPC framework provides a unified, robust, and efficient solution for quadrotor formation control under both ideal and perturbed environments.
Li, Jiaye, Zhang, Zexu, Liu, Jingzong
Formal Verification of a Generic Algorithm for Universal TDM Communication Using UPPAAL2026-99-298610/19/2026
This paper describes formal modeling and verification of generic algorithm for universal Time Division Multiplexing (TDM) communication, one of the orchestration algorithms used in PTB-FLA (Python TestBed for Federated Learning Algorithms), a light-weight framework for developing distributed applications, developed as a part of the TaRDIS (Trustworthy and Resilient Decentralized Intelligence For Edge Systems) project. Modeling and verification is done using UPPAAL model checker tool. When developing a distributed application, in early development phases a user of PTB-FLA can abstract away low-level details of message passing and some elements of orchestration, by choosing one of the available orchestration algorithms, therefore being able to focus on the actual distributed algorithm at the application level. The generic algorithm for universal TDM communication models peer data exchange between the nodes within a discrete time-slots, a frequent case in inter-satellite links. It is an orchestration algorithm intended to be used in satellite-swarm applications, such as distributed ODTS (Orbit Determination and Time Synchronization). Formal modeling and formal verification contributes significantly to increased reliability of orchestration layer, making development of a distributed algorithm easier by narrowing down possible sources of errors to application level. This paper gives overview of PTB-FLA framework, provides a description of the orchestration algorithm, shows its formal model in UPPAAL, and verifies some important algorithm properties, namely deadlock freedom (a safety property) and successful termination (a liveness property). This paper represents a continuation of one of the research lines related to PTB-FLA, which is formal verification of the framework’s generic orchestration algorithms, and other elements.
Djukic, Miodrag, Popovic, Miroslav, Li, Yuqing
Performance Prediction of Hall Thrusters with Deep Learning Algorithms2026-99-298810/19/2026
Accurate performance prediction is essential for improving the reliability and service life of Hall thrusters in space applications. Because conventional scaling-based prediction methods are often limited when the operating state and magnetic-field structure become complex, this study develops a deep-learning framework for rapid prediction of key Hall-thruster performance indicators. Three representative neural-network architectures, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM), are constructed and comparatively evaluated. Among them, BiLSTM achieves the best overall performance, with a Mean Absolute Percentage Error (MAPE) of 6.44% on the test set. Additional validation based on real Hall-thruster experimental data gives a MAPE of 3.884%, indicating that the proposed model has good generalization ability and can provide an effective data-driven tool for Hall-thruster performance prediction.
Sun, Chang-You, Tian, Bin, Yu, Yun-Chao, Wang, Ke, Li, Shu-Feng, Xie, Kan, Yang, Su-Lan
Friction Dynamics Modeling and System Simulation of Large-Angular-Momentum Flywheels for Satellites2026-99-296410/19/2026
In response of the stringent requirements of high-resolution optical remote sensing satellites for attitude control stability and micro-vibration environment, this study focuses on the friction dynamics modeling and simulation analysis of large-angular-momentum flywheels across a wide speed range. To quantify the impact of flywheel friction disturbances on satellite attitude pointing accuracy, friction torque characteristic data were first acquired through ground testing over the full speed range, and a high-fidelity static fitting mathematical model was constructed using the least squares method. To further elucidate the nonlinear friction mechanisms in the low-speed regime—particularly during speed zero-crossings—the LuGre dynamic friction model was introduced, and its core parameters were identified and optimized. Comparative results demonstrate that the model exhibits high-precision characterization capabilities, with a deviation of less than 0.0015 Nm from measured data within the critical low-speed range of ±50 rpm. Subsequently, the established friction model was integrated into a satellite attitude control closed-loop system for digital simulation to quantitatively evaluate the disturbance of friction torque on system steady-state accuracy. The results indicate that under the closed-loop control compensation mechanism, the impact of flywheel friction on satellite attitude pointing accuracy is effectively suppressed, with the attitude angle deviation maintained within 0.002°, thereby meeting the engineering requirements for high-precision remote sensing imaging. This research provides a theoretical basis and data support for the optimization design of flywheel systems and the refined design of satellite attitude controllers.
Hu, Yuewei, Liang, Zhulin, Lu, Ming, Zhang, Pengbo, Hu, Xiaozhen
A Health State Assessment Method Based on Autoencoder and Analytic Hierarchy Process for Spacecraft Components2026-99-297310/19/2026
Health assessment of complex systems mainly relies on expert experience or a data-driven health assessment model trained by a large volume of historical condition monitoring data. However, only relying on expert experience to handle the health information contained in a large amount of telemetry data is very time-consuming and labor-intensive for spacecraft, and it does not make full use of a large amount of historical data. Moreover, due to limitations such as space-ground data transmission resources, it is also difficult to obtain all the condition monitoring parameters at each system level for different health states. In response to these challenges, this paper proposes a health assessment framework combining expert experience and data-driven strategy to achieve a multi-level quantitative assessment for spacecraft components. Specifically, a deep autoencoder is trained to construct the state change curves of the key condition monitoring parameters, and the quantitative score map is constructed for each parameter. Then, by analyzing the matrix of pairwise importance for parameters based on expert experience, the health status change curve at a second level of the component is constructed. Finally, a quantitative score map of the component status is provided using the health status change curve to realize the multi-level quantitative health assessment. A case study on health assessment for an attitude control actuator is conducted to show the overall procedure and effectiveness of the health assessment framework. The results demonstrate that the proposed method can effectively integrate expert knowledge and data characteristics, providing a feasible framework for the multi-level health assessment of spacecraft components.
Dong, Yunjia, Li, Yuqing
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
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
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
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
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
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
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
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
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