On-orbit Satellite Research Articles

Estimation of direction and zero errors of satellite laser terminals in low-light conditions based on machine learning

In the assembly, launch, and on-orbit operation of satellite optical communication terminals, small deviations are difficult to avoid, which can lead to pointing errors and challenges to the establishment of optical communication links. To estimate the pointing errors of on-orbit satellite terminals, a calibration algorithm is developed based on lunar surface imagery. First, a feature extraction algorithm for low-light images is employed to process consecutive frames of low-light images to obtain a lunar surface feature map. Then, by combining the feature map and error estimation model, predictions of direction errors and zero errors were achieved. The ground validation results demonstrate the effectiveness and feasibility of the proposed on-orbit error estimation algorithm under low-signal-to-noise-ratio conditions.

On-orbit satellite hierarchical anomaly detection using causal structure learning

Real-time anomaly detection for on-orbit satellites is crucial in the early identification of faults to prevent further anomaly expansion. However, current anomaly detection methods for on-orbit satellites face challenges such as low sensitivity to anomalies, high rates of false negatives, and a lack of criteria for assessing anomaly severity. The Spatio-Temporal Causal Graph (STCG) describes the intrinsic temporal and spatial relationships among telemetry parameters. By extracting temporal and spatial features, it facilitates enhanced modeling of normal data and improves the model’s sensitivity to anomalies. Moreover, according to the principle of anomaly propagation, the STCG can be utilized to identify false negatives. Therefore, we propose a framework of on-orbit satellite hierarchical anomaly detection using causal structure learning (OSHAD-CSL). This framework introduces a graph autoencoder for STCG learning and develops a method for dynamically setting detection thresholds. Additionally, the framework puts forward a false negative identification method based on the learned STCG. Lastly, the framework presents an anomaly severity judgment criterion, enabling the determination of the affected system level by the detected anomaly. To evaluate the effectiveness of the proposed framework, we validate the OSHAD-CSL on three public datasets and a dataset from a satellite’s attitude control system. The results demonstrate that the proposed method outperforms the baselines, with an F1-score (a metric used for evaluating the performance of anomaly detection) of 80% for anomaly detection and an accuracy of 83% for anomaly severity judgment. These findings highlight the efficacy of the proposed method in improving the performance of anomaly detection and severity assessment.

Maintenance satellite modular docking mechanism design for on orbit servicing to nanosatellites

PurposeAs a result of space debris problem, it is necessary to deorbit uncontrollable satellites or repair them to extend mission duration to avoid sending a new satellite. The purpose of this paper is to develop a docking mechanism that can be easily customized for different missions, providing on-orbit servicing for nanosatellites.Design/methodology/approachThis research outlines a system and mechanism design for the docking phase of on-orbit servicing to nanosatellites. The umbrella-inspired mechanism is designed with utmost simplicity to minimize the likelihood of failure. CAD, structural analyse and mechanism analyse tools are used for designing and analysing the system. To ensure that the design attains the desired durability, numerous iterations are conducted. A three-dimensional (3D)-printed prototype is generated for mechanism verification in laboratory conditions.FindingsThe aimed mechanism is generated successfully. A 3D-printed prototype is assembled to verify the mechanism. Also, an equation for customis the presented design is generated for different mission requirements in the future.Practical implicationsThe usage of the proposed design can help increase the lifespan of satellites.Originality/valueThe primary innovation in this study is the development of a docking mechanism featuring a movable platform to provide servicing for nanosatellites in orbit. The mechanism presented can be displaced without initiating the unfolding process. This provides a customizable coupling distance for different mission requirements. Therefore, the presented mechanism can serve both different types of satellites and more than one satellite on-orbit with a cost-effective design. Also, the presented design can be easily customized to enable adaptation for the different mission requirements in the future.

A Multi-Pointer Network for Multiple Agile Optical Satellite Scheduling Problem

With the rapid growth in space-imaging demands, the scheduling problem of multiple agile optical satellites has become a crucial problem in the field of on-orbit satellite applications. Because of the considerable solution space and complicated constraints, the existing methods suffer from a huge computation burden and a low solution quality. This paper establishes a mathematical model of this problem, which aims to maximize the observation profit rate and realize the load balance, and proposes a multi-pointer network to solve this problem, which adopts multiple attention layers as the pointers to construct observation action sequences for multiple satellites. In the proposed network, a local feature-enhancement strategy, a remaining time-based decoding sorting strategy, and a feasibility-based task selection strategy are developed to improve the solution quality. Finally, extensive experiments verify that the proposed network outperforms the comparison algorithms in terms of solution quality, computation efficiency, and generalization ability and that the proposed three strategies significantly improve the solving ability of the proposed network.

Detection and Analysis of Anomalous Behavior in On-Orbit Satellites Using AI Algorithms

The increasing deployment of satellites for essential applications necessitates robust anomaly detection to maintain their operational integrity. Traditional methods, which depend on manual monitoring and predefined thresholds, often prove inadequate in the complex space environment. This paper investigates the application of Artificial Intelligence (AI) algorithms to improve the detection and analysis of anomalous behavior in on-orbit satellites. AI, especially through machine learning (ML) and deep learning (DL), provides advanced capabilities for processing extensive telemetry data and identifying intricate patterns. By leveraging historical data, AI systems can establish normal operational parameters and detect deviations indicating potential anomalies. Techniques such as supervised and unsupervised learning are employed to develop models with high predictive accuracy. Furthermore, AI facilitates root cause analysis by correlating anomalies with operational conditions or external factors, enabling effective corrective measures. The integration of AI also promotes autonomous satellite operations, which are crucial for deep-space missions. This advancement enhances satellite reliability and safety, supporting sustainable and progressive space exploration. In this research, machine learning algorithms were employed to develop the proposed anomaly detection system. The system aims to detect subtle failures in the spacecraft’s attitude dynamics system, particularly in the reaction wheel subsystem, by learning solely from the spacecraft's nominal behavioral data. The system was developed from a small satellite's attitude dynamics control system, which may exhibit bearing failures in the reaction wheels. Two types of anomaly detection systems were introduced: a two-sided learning anomaly detection system and a one-sided learning anomaly detection system. For this study, a two-sided learning anomaly detection system was developed using the Logistic Regression (LR) method. This provided a foundation for the training process using a machine learning approach. By learning from both nominal and failure behaviors of the satellite, the system was designed to detect small reaction wheel friction failures effectively.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

Deep Q-Learning for Decentralized Multi-Agent Inspection of a Tumbling Target

As the number of on-orbit satellites increases, the ability to repair or de-orbit them is becoming increasingly important. The implicitly required task of on-orbit inspection is challenging due to coordination of multiple observer satellites, a highly nonlinear environment, a potentially unknown or unpredictable target, and time delays associated with ground-based control. There is a critical need for autonomous, robust, decentralized solutions. To achieve this, we consider a hierarchical, learned approach for the decentralized planning of multi-agent inspection of a tumbling target. Our solution consists of two components: a viewpoint or high-level planner trained using deep reinforcement learning, and a low-level planner that will handle the point-to-point maneuvering of the spacecraft. Operating under limited information, our trained multi-agent high-level policies successfully contextualize information within the global hierarchical environment and are correspondingly able to inspect over 90% of nonconvex tumbling targets, even in the absence of additional agent attitude control.

An Effective Space-Borne ISAR High-Resolution Imaging Approach for Satellite On-Orbit Based on Minimum Entropy Optimization

An Effective Space-Borne ISAR High-Resolution Imaging Approach for Satellite On-Orbit Based on Minimum Entropy Optimization

Satellite Component Semantic Segmentation: Video Dataset and Real-Time Pyramid Attention and Decoupled Attention Network

High-accuracy and real-time satellite component semantic segmentation can locate the key satellite components, such as solar panels, to be operated in on-orbit services, which is of great significance for navigation and control. However, to accomplish the above aim, two main challenges remain unsolved. Firstly, satellite component semantic segmentation algorithms require a large number of images for training; however, on-orbit satellite images are difficult to obtain, especially for a large-scale satellite component video dataset. In addition, high-accuracy semantic segmentation networks require relatively more computation resources which are difficult to be fulfilled in on-orbit tasks. How to build a satellite component semantic segmentation network that meets the requirements of both high-accuracy and real-time on-orbit operation is the key aim to be accomplished in this paper. In this paper, a simulated satellite component dataset consisting of 98 video sequences of 13 satellites, with complex background, various on-orbit illumination and common satellite motion, is proposed, and it has 32402 frames in total. To meet the requirements of both high-accuracy and real-time on-orbit operation, this paper proposes an attention-based real-time network, Pyramid Attention and Decoupled Attention Network (PADAN), which contains an image-based version, PADAN-S, and a video-based version, PADAN-T. The PADAN-S, which mainly adopts pyramid attention calculation on three-layer pyramid features and then performs decoupled attention calculation by considering both row and column attention, is based on AttaNet. The PADAN-T uses a part of the PADAN-S to obtain temporal pyramid features from temporal frames, then performs decoupled attention calculations between the features of output frame and the features at each layer in temporal pyramid. The experimental results show that the PADAN-S and PADAN-T have superior performance compared to other real-time state-of-the-art algorithms in accuracy in both image-based and video-based satellite component semantic segmentation tasks on simulation datasets, and our dataset has a degree of simulating the real on-orbit environment. The PADAN-S can achieve a speed of 10.25 frames per second with image solution of 1280pixels×720pixels on the edge computing device Jetson Xavier, and the PADAN-T can obtain a speed of 7.18 frames per second.

Multitrajectory Combination for Multiple Ground Target Observation by Maneuvering On-Orbit Satellites

Multitrajectory Combination for Multiple Ground Target Observation by Maneuvering On-Orbit Satellites

A Vision-Based Pose Estimation of a Non-Cooperative Target Based on a Self-Supervised Transformer Network

In the realm of non-cooperative space security and on-orbit service, a significant challenge is accurately determining the pose of abandoned satellites using imaging sensors. Traditional methods for estimating the position of the target encounter problems with stray light interference in space, leading to inaccurate results. Conversely, deep learning techniques require a substantial amount of training data, which is especially difficult to obtain for on-orbit satellites. To address these issues, this paper introduces an innovative binocular pose estimation model based on a Self-supervised Transformer Network (STN) to achieve precise pose estimation for targets even under poor imaging conditions. The proposed method generated simulated training samples considering various imaging conditions. Then, by combining the concepts of convolutional neural networks (CNN) and SIFT features for each sample, the proposed method minimized the disruptive effects of stray light. Furthermore, the feedforward network in the Transformer employed in the proposed method was replaced with a global average pooling layer. This integration of CNN’s bias capabilities compensates for the limitations of the Transformer in scenarios with limited data. Comparative analysis against existing pose estimation methods highlights the superior robustness of the proposed method against variations caused by noisy sample sets. The effectiveness of the algorithm is demonstrated through simulated data, enhancing the current landscape of binocular pose estimation technology for non-cooperative targets in space.

SOCDet: A Lightweight and Accurate Oriented Object Detection Network for Satellite On-Orbit Computing

In recent years, the performance of the deep learning based object detection models for remote sensing images improves with the increase of the hyperparameter scale. However, the state of the art of detection models are generally too cumbersome to adapt to the resource-constrained satellite platforms, and even general lightweight detection models cannot satisfy performance requirements. To solve those problems, a lightweight and accurate oriented object detection network for satellite on-orbit computing (SOCDet) is proposed from three levels. At the computing unit level, we propose an efficient computing unit Single-kernel Omni-dimensional Dynamic Convolution to make SOCDet feature extraction more efficient. At the network module level, we design a structural reparameterization block based on composite structure reparameterization to improve inference accuracy. A guided attention module for guiding SOCDet is designed to extract object features. We design a lightweight and concise one-stage detection architecture at the network architecture level to accommodate satellite platforms with extremely constrained computing and storage resources. We conduct ablation experiments on the ground server and compare experiments with the state-of-the-art methods on embedded GPU, using DOTA, HRSC2016 and FAIR1M. The experiment results show that SOCDet can achieve 2.36 times faster than the baseline in inference speed with 10.78 more mAP, 72.6% fewer Params and 92.56% fewer floating point operations. Compared with state-of-the-art methods, SOCDet improves the inference speed and derives competitive mAP results with affordable computing capability.

A Convolutional Dynamic-Jerk-Planning Algorithm for Impedance Control of Variable-Stiffness Cable-Driven Manipulators.

Cable-driven manipulators, characterized by slender arms, dexterous motion, and controllable stiffness, have great prospects for application to capture on-orbit satellites. However, it is difficult to achieve effective motion planning and stiffness control of cable-driven manipulators because of the coupled relationships between cable lengths, joint angles, and reaction forces. Therefore, a convolutional dynamic-jerk-planning algorithm is devised for impedance control of variable-stiffness cable-driven manipulators. First, a variable-stiffness cable-driven manipulator with universal modules and rotary quick-change modules is designed to overcome difficulties related to disassembly, installation, and maintenance. Second, a convolutional dynamic-jerk-planning algorithm is devised to overcome the discontinuity and shock problems of the manipulator's velocity during intermittent control processes. The algorithm can also make acceleration smooth by setting jerk dynamically, reducing acceleration shock and ensuring the stable movement of the cable-driven manipulator. Third, the stiffness of the cable-driven manipulator is further optimized by compensating for the position and velocity of drive cables by employing position-based impedance control. Finally, the prototype of the variable-stiffness cable-driven manipulator is developed and tested. The convolutional dynamic-jerk-planning algorithm is used to plan the desired velocity curves for velocity control experiments of the cable-driven manipulator. The results verify that the algorithm can improve the acceleration smoothness, thereby making movement smooth and reducing vibrations. Furthermore, stiffness control experiments verify that the cable-driven manipulator has ideal variable stiffness capabilities.

Dynamic Estimation of Spin Satellite From the Single-Station ISAR Image Sequence With the Hidden Markov Model

With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work.

Analysis of the Long-Term Characteristics of BDS On-Orbit Satellite Atomic Clock: Since BDS-3 Was Officially Commissioned

Satellite atomic clocks are the key elements for position, navigation, and timing services of the Global navigation satellite system (GNSS); it is necessary to research the characteristics of BDS-3 on-orbit satellite atomic clocks for their further optimization. In this study, clock offset data with a duration of 620 days since BDS-3 was officially commissioned were applied to long-term characteristic analysis. To begin with, the precision clock offset data of Deutsches geoforschungs zentrum (GFZ) processed by a MAD-based method were used as reliable test data. Herein, the working principle and main characteristics of satellite atomic clocks are analyzed and discussed, and thus, a comprehensive long-term characteristic analysis scheme is designed. On this basis, the performance indicators—mainly including physical parameters, periodic characteristics, frequency drift rate, frequency accuracy, frequency stability—were calculated and analyzed respectively, revealing the long-term characteristics of the BDS in orbit satellite atomic clocks during the test period. The results of experimental data testify that the performance of BDS-3 satellite atomic clocks is significantly superior to that of BDS-2, especially in terms of drift rate and frequency stability, and the performance of passive hydrogen maser (PHM) is generally superior to that of rubidium atomic frequency standards (RAFS). Within about half a year since BDS-3 was officially commissioned, the frequency stability of BDS-3 satellite atomic clock gradually improved and then reached the order of 10−15, reflecting the effectiveness of system maintenance and inter-satellite link. Furthermore, some novel conclusions are drawn, such as the long-term period term of the fitting residual and drift rate, which may be caused by the earth’s revolution.

Simulating hypervelocity impact with a discrete element approach

We present new developments to a discrete element model designed to simulate hypervelocity impacts between on-orbit satellites and space debris. The advantage of the discrete element-based simulation method is its ability to accurately model fragmentation which always accompanies a hypervelocity impact event. As previous work showed the model's ability to accurately simulate hypervelocity impact fragmentation at very high impact velocities, our focus in this paper is predominantly on improving the model's behavior at lower velocities. These low-velocity secondary-impacts also play an important role in determining the final distribution of fragments following a hypervelocity impact event. We achieve this by enhancing our discrete element model's bonding structure to more accurately simulate a continuum material. We evaluate the enhanced model's performance with a variety of simulations ranging from tension tests to hypervelocity impacts. Parameters for the model are calibrated for the whole range of impact velocities using well know ballistic limit equations. Three large impact simulations are presented which demonstrate the model's capabilities in capturing the full range of fragment sizes. The satellite breakup simulation results are compared to the NASA breakup model and show very good agreement.

Dynamical reliability analysis and remaining useful life prediction for an on-orbit satellite camera

Launch missions of small satellites have been increasing in recent years. As the main payload, the camera plays a pivotal role in remote sensing detection. Therefore, on-orbit health estimation is a continuous concern for satellite design. This study aims to develop a dynamic intelligent health estimation algorithm for the on-orbit satellite camera. Based on fuzzy theory in the intelligent health estimation algorithm, the reliability and the remaining useful life are transformed into a newly defined health estimation index, namely “health vector,” which reflects camera health from the aspects of the electrical and mechanical parts. Compared with traditional methods, the new intelligent health estimation algorithm can not only judge the development direction of satellite camera’s health status, but also predict the degree of change. In addition, the proposed “health vector” is concise for engineers to monitor the health status of satellites in real time from the ground in multiple dimensions. In summary, this study provides a new method for on-orbit health management of satellite cameras.

Using Clean Energy Satellites to Interpret Imagery: A Satellite IoT Oriented Lightweight Object Detection Framework for SAR Ship Detection

This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous development of the commercial aerospace industry, computing, and high-speed laser inter-satellite link technologies, the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. On-orbit interpretation gives remote sensing images expanse application space. However, implementing on-orbit high-performance computing (HPC) is difficult; it is limited by the power and computer resource consumption of the satellite platform. Facing this challenge, building a processing algorithm with less computational complexity, less parameter quantity, high precision, and low computational power consumption is a key issue. In this paper, we propose a lightweight end-to-end SAR ship detector fused with the vision transformer encoder: YOLO−ViTSS. The experiment shows that YOLO−ViTSS has lightweight features, the model size is only 1.31 MB; it has anti-noise capability is suitable for processing SAR remote sensing images with native noise, and it also has high performance and low training energy consumption with 96.6 mAP on the SSDD dataset. These characteristics make YOLO−ViTSS suitable for porting to satellites for on-orbit processing and online learning. Furthermore, the ideas proposed in this paper help to build a cleaner and a more efficient new paradigm for remote sensing image interpretation. Migrating HPC tasks performed on the ground to on-orbit satellites and using solar energy to complete computing tasks is a more environmentally friendly option. This environmental advantage will gradually increase with the current construction of large-scale satellite constellations. The scheme proposed in this paper helps to build a novel real-time, eco-friendly, and sustainable SAR image interpretation mode.

An experimental investigation of slat-screens to mitigate fluid sloshing in microgravity

The new age of space exploration has brought forth growing implications of space debris connected to satellites, and as such on-orbit satellite refuelling is an area of active research. To refuel a satellite on-orbit, a servicing satellite transfers propellant to the empty fuel tank of the satellite being serviced. A significant impediment to such a practice is the development of fluid sloshing motion during this process. This paper presents a study of the use of slat screens to damp fluid sloshing motion during a simulated refuelling of a fuel tank under microgravity conditions. There is particular interest in the sloshing motion developed due to internal forces produced from fluid injection. Two fluids, each analogous to common spacecraft propellants; hydrazine and liquid oxygen (LOX), were tested under a microgravity environment created by parabolic manoeuvres aboard the National Research Council of Canada's (NRC) Falcon 20 research aircraft. The experimental setup included three test chambers, two of which contained a perforated and a single-entry slat screen, designed to mitigate sloshing. High-speed imaging of fluid sloshing development within the test chambers was captured and analysed. The rate of fuelling was varied during subsequent parabolas to 25%, 50% and 75% of the internal volume capacity (1 L). Based on initial findings, statistical differences exist (P<0.05) in the latency for the LOX analogue, to reach 75% and 95% of the total chamber height. Differences were observed specifically between the control chamber and the chamber with perforated slat screens. Furthermore, qualitative observations of the hydrazine analogue fluid motion in microgravity show that at 50% fill ratio, slat screens result in a plateau of maximum fluid height. This suggests that slat screens may be an effective method of damping fluid sloshing motion and could be adopted by industry in the pursuit of on-orbit refuelling and the development of more sustainable spacecraft.

Estimation of Orbital Parameters of Broken-Up Objects From In-Situ Debris Measurements

This paper briefly introduces a new approach to estimate some orbital parameters of on-orbit satellite fragmentations (specifically, the direction of angular momentum at a specific time and the time change in direction of angular momentum) from in-situ debris measurements. This approach, as in previous studies, adopts a constraint equation derived from the fact that a piece of debris detected shares the geocentric position vector with an in-situ debris measurement satellite. However, unlike previous studies, this approach does not adopt a constraint equation that can be applied to the rate of change in right ascension of the ascending node of a broken-up object. Instead, this approach determines the inclination of a broken-up object from the maximum or minimum geocentric declination at the time of detection. Then, this approach finds out a candidate for the rate of change in right ascension of the ascending node of a broken-up object by assuming a circular orbit with a radius of the geocentric distance at the time of detection. Finally, using the constraint equation adopted, this approach estimates the right ascension of the ascending node at the time of breakup and calculates a correction for the rate of change in right ascension of the ascending node. This paper also verifies that this new approach works effectively under ideal conditions where all detections are assumed to be at the line of intersection of the two orbital planes of a broken-up object and an in-situ debris measurement satellite.