In-orbit Satellite Research Articles

Multi-Layer Objective Model and Progressive Optimization Mechanism for Multi-Satellite Imaging Mission Planning in Large-Scale Target Scenarios

With the continuous increase in the number of in-orbit satellites and the explosive growth in the demand for observation targets, satellite resource allocation and mission scheduling are faced with the problems of declining benefits and stagnant algorithm performance. This work proposes a progressive optimization mechanism and population size adaptive strategy for an improved differential evolution algorithm (POM-PSASIDEA) in large-scale multi-satellite imaging mission planning to address the above challenges. (1) MSIMPLTS based on Multi-layer Objective Optimization is constructed, and the MSIMPLTS is processed hierarchically by setting up three sub-models (superstructure, mesostructure, and understructure) to achieve a diversity of resource selection and step-by-step refinement of optimization objectives to improve the task benefits. (2) Construct the progressive optimization mechanism, which contains the allocation optimization, time window optimization, and global optimization phases, to reduce task conflicts through the progressive decision-making of the task planning scheme in stages. (3) A population size adaptive strategy for an improved differential evolution algorithm is proposed to dynamically adjust the population size according to the evolution of the population to avoid the algorithm falling into the local optimum. The experimental results show that POM-PSASIDEA has outstanding advantages over other algorithms, such as high task benefits and a high task allocation rate when solved in a shorter time.

Large-Scale Fabrication of Customized, Tunable, Ultrathin, and Flexible Metamaterial Absorbers Based on Laser-induced Graphene

Developing various 3D graphene microwave absorbers is a hotspot strategy to address the issue of continuous electromagnetic pollution, but remains great challenges in realizing high-performance microwave absorption capability at thin thickness. Herein, a series of flexible metamaterial absorbers (MAs) composed of periodical laser-induced graphene (LIG) surface, dielectric substrate and metallic ground fabric from top to bottom are fabricated. The LIG meta-surface is customized by means of regulating parameters including laser power density (LPD), laser engraving rate (LER) and input patterns to well balance impedance matching and electromagnetic loss. LIG MAs exhibit minimum reflection loss (RL) of −39.7 dB (with effective absorption bandwidth (EAB) of 5.5 GHz) and maximum EBA of 12.1 GHz (with RL of −18.9 dB) at thickness less than 1.1 mm, and the simulated and experimental results are highly consistent. In addition, radar cross section (RCS) simulation of LIG MA with multi-angles endows its potential for prospective applications in in-orbit satellites electromagnetic protection.

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.

A Multiple Agile Satellite Staring Observation Mission Planning Method for Dense Regions

To fully harness the burgeoning array of in-orbit satellite resources and augment the efficacy of dynamic surveillance of densely clustered terrestrial targets, this paper delineates the following methodologies. Initially, we leverage the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to aggregate the concentrated terrestrial targets, taking into account the field-of-view peculiarities of agile staring satellites. Subsequently, we architect a model for a synergistic multiple angle earth observation satellites (AEOSs) mission planning with the optimization objectives of observational revenue, minimal energy expenditure, and load balancing, factoring in constraints such as target visibility time window, AEOSs maneuverability, and satellite storage. To tackle this predicament, we propose an improved heuristic ant colony optimization (ACO) algorithm, utilizing the task interval, task priority, and the length of time a task can start observation as heuristic information. Furthermore, we incorporate the notion of the max–min ant system to regulate the magnitude of pheromone concentration, and we amalgamate global and local pheromone update strategies to expedite the convergence rate of the algorithm. We also introduce the Lévy flight improved pheromone evaporation coefficient to bolster the algorithm’s capacity to evade local optima. Ultimately, through a series of simulation experiments, we substantiate the significant performance improvements achieved by the improved heuristic ant colony algorithm compared to the standard ant colony algorithm. We furnish proof of its efficacy in resolving the planning of multiple AEOS staring observation missions.

Degradation and Multi-Time-Scale State Estimation of Li-Ion Batteries in Satellites

In-orbit satellite REIMEI, developed by the Japan Aerospace Exploration Agency (JAXA), has been relying on off-the-shelf Li-ion batteries since its launch in 2005 [1]. The performance and durability of Li-ion batteries is impacted by various degradation mechanisms, one of which is the growth of the solid-electrolyte interphase (SEI). Long-term SEI growth is the greatest contributor to capacity fade in lithium-ion batteries.In this contribution, we will address several aspects of the modeling and simulation of the batteries of satellite REIMEI. We simulate long-term degradation under the generic LEO satellite cycling conditions in a P2D framework. The simulations are validated with experiments and in-flight data provided by JAXA [1]. Our group has developed models for long-term SEI growth [2,3]. To show the inhomogeneous growth of the SEI in 3D, we perform microstructure-resolved simulations [4].These studies are the foundation for analyzing the states of the batteries, which cannot be measured in operando. To estimate the state of charge and state of health, we make use of filter techniques and the in-flight data of the satellite batteries. Kalman filters are particularly suitable for the noisy data. Since the states change on different timescales, a multi-time-scale algorithm is applied, where two filters are combined. With this approach, we aim to reliably predict the lifetime of satellite batteries in orbit.

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications.

Polarization response characteristics of 6061Al and PMMA sheets under impact load

The material will induce shock polarization under the action of shock wave. Aluminum alloys (such as 6061Al) are used as the main material for spacecraft skins, panels and frames. The impact polarization effect generated during the cratering deformation (compression and shearing) of space debris will induce the surface discharge of the orbiting satellite. Polymethyl methacrylate (PMMA) materials are often used in combination with electronic devices of weapon equipment. Under high overload, PMMA will produce shock polarization effect, electromagnetic interference on electronic devices, and even trigger by mistake. Therefore, it is of great significance to reveal the impact polarization characteristics of different types of materials (metal, polymer) for improving the bulletproof protection ability of in-orbit satellites, protective armor and electronic devices. In this paper, the 6061Al and PMMA thin plates were taken as the research object, and the universal testing machine combined with LCR was used to measure the capacitance and resistance of 6061Al and PMMA under different given loads by combining theoretical analysis, experiment and simulation. At the same time, the first stage light gas gun is used to study the local impact induced electro-mechanical response of metal and polymer sheet materials. On this basis, the AUTODYN-ABAQUS/Explicit-COMSOL co-simulation under corresponding experimental conditions was carried out to obtain the distribution rules of temperature and strain gradient. Based on the theory of flexoelectric effect, the impact polarization response characteristics of 6061Al and PMMA materials were clarified, respectively. The results show that the impulse polarization voltage of 6061Al thin plate increases first and then gradually decreases with time. The impulse polarization voltage is proportional to the thickness. The polarization voltage of single-electrode PMMA thin plate is parabola; the polarization voltage of PMMA thin plate with two electrodes increases first and then gradually decreases. The absolute value of the strain gradient of the 6061Al thin plate increases first and then becomes stable, while the strain gradient of the PMMA thin plate increases first and then decreases.

Health condition assessment of satellite li-ion battery pack considering battery inconsistency and pack performance indicators

Assessing the health condition of satellite li-ion battery pack accurately is critical for determining the safety of satellite power supply and ensuring that satellite satisfies its service life. Since the limitations of space environment make satellite battery special in operation and maintenance, misjudgments may occur when using existing assessment methods. To solve this problem, a novel health condition assessment indicator system that integrates battery inconsistency and pack performance indicators is built according to the performance requirement of battery pack. Based on the typical strategy of satellite battery pack performance degradation management, equalization and bypass state are employed as factors to make assessment result more accurate and comprehensive. Then, a method based on ER (evidential reasoning) and BRB (belief rule base) is proposed to achieve inconsistencies fusion and rule-based assessment. And the results can be expressed as belief degree distribution or score. Finally, the health condition of a certain type of in-orbit satellite's li-ion battery pack is assessed using telemetry data. The results are consistent with the battery's actual working state, which proves the effectiveness of the proposed method.

Requirement-Oriented TT&C Method for Satellite Based on BDS-3 Short-Message Communication System

With the increasing number of satellites being launched in China, the demand for Telemetry, Tracking, and Command (TT&C) resources is also increasing. Because of the global coverage and resource requirements of TT&C management, this paper proposes a TT&C scheme based on the Global Short-Message Communication System (GSMCS) of the BeiDou-3 Navigation Satellite System (BDS-3). The system composition, workflow, and functions of satellite TT&C are analyzed using the GSMCS of BDS-3. Through the coverage analysis of the Medium Earth Orbit (MEO) constellation of BDS-3, scheme’s applicability is pointed out, and the correctness of the analysis results is verified via simulation. The value range of the link distance is derived by calculating the range of the distance variation between any 2 satellites in the BDS-3 MEO constellation and combining the intersatellite routing scheme. The calculation results show that the response time range of the autonomous request management application of in-orbit satellites based on the GSMCS of BDS-3 is 1.407 to 2.448 s. This can meet the requirements of TT&C service.

HE[formula omitted]LM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network

Satellite attitude determination, as a crucial pre-processing technology in remote sensing earth observation, is significantly associated with the geometric accuracy of satellite stereo images. To compensate for the observation errors in raw attitude data for achieving accurate results, the conventional Kalman filter-based and filter-smoothing combination frameworks provide insufficient access to statistical features of data, resulting in inferior practical system performance. To conquer these drawbacks, a hierarchical and efficient attitude determination framework is proposed called HE2LM-AD, which consists of a simplified adaptive Kalman filtering module, a neural network-based system error compensation module, and a weighted attitude smoothing module. To be more specific, an adaptive noise covariance matrix update is exploited in the Extended Kalman Filter (EKF) iterations, and a simplification scheme based on Cholesky decomposition is employed to obtain coarse filter outputs efficiently. Following that, a dedicated Extreme Learning Machine (ELM)-based error compensation network is designed to capture the pattern of observation errors, providing robust compensation at lower overhead. To further pursue high-performance attitude determination results, we fused the outcomes of the aforementioned two components according to the weights achieved from adaptive learning. Experiment results on in-orbit satellite attitude simulation datasets demonstrate and validate the feasibility and effectiveness of our framework.

Blind Deblurring of Remote-Sensing Single Images Based on Feature Alignment.

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects.

Prediction Algorithm for Satellite Instantaneous Attitude and Image Pixel Offset Based on Synchronous Clocks

To ensure a high signal-to-noise ratio and high image volume, a geostationary orbiting ocean remote-sensing system needs to maintain high platform stability over a long integration time because it is affected by satellite attitude changes. When the observation target is the ocean, it is difficult to extract image features because of the lack of characteristic objects in the target area. In this paper, we attempt to avoid using image data for satellite attitude and image pixel offset estimation. We obtain the satellite attitude by using equipment such as gyroscopes and performing time registration between the satellite attitude and the image data to achieve pixel offset matching between images. According to the law of satellite attitude change, we designed a Kalman-like filter fitting (KLFF) algorithm based on the satellite attitude change model and the Nelder–Mead search principle. The discrete attitude data were time-marked by a synchronization system, and high-precision estimation of the satellite attitude was achieved after fitting with the KLFF algorithm. When the measurement accuracy of the equipment was 1.0 × 10−3°, the average prediction error of the algorithm was 1.09 × 10−3°, 21.58% better than the traditional interpolation prediction result of 1.39 × 10−3°. The peak value of the fitting angle error reached 2.5 × 10−3°. Compared with the interpolation prediction result of 6.2 × 10−3°, the estimated stability of the satellite attitude improved by about 59.68%. After using the linear interpolation method to compensate for the estimated pixel offset, its discrete range was 0.697 pixels. Compared with the 1.476 pixels of the interpolation algorithm, it was 52.8% lower, which improved the noise immunity of the algorithm. Finally, a KLFF algorithm was designed based on the satellite attitude change model by using external measurement data and the synchronous clock as a benchmark. The instantaneous attitude of the satellite was accurately estimated in real time, and the offset matching between the images was realized, which lays the foundation for in-orbit satellite data processing.

End-of-discharge prediction for satellite lithium-ion battery based on evidential reasoning rule

Abstract To ensure the safety of the power supply for an in-orbit satellite, it is of great significance to accurately predict the end-of-discharge time of lithium-ion batteries for making a reasonable flight plan. Constrained by development time and experimental environment, it is usually difficult to obtain many full discharge voltage curves of satellite batteries from ground experiments as historical data. Because of insufficient data, the prediction accuracy of the single time series prediction method is low. To solve this problem, this paper takes the voltage of the discharge process as the time series and uses the evidential reasoning rule algorithm to fuse the outputs of three typical prediction models to improve the prediction accuracy. The result can be expressed as a form of belief degree distribution with the ability to express uncertainty. Using the NASA battery dataset, the effectiveness of the proposed method is verified, and the end-of-discharge of an in-orbit satellite battery is predicted by the telemetry data.

Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model

To ensure safety of satellite operation in orbit, it is important to estimate the capacity of lithium-ion battery in time. However, the battery capacity cannot be measured directly in space, and it is continually changing due to the usage that changes with the space environment. Because of the complex electrochemical side reactions inside battery, it is difficult to establish an accurate model. To solve the above problems, eight features from battery usage of three processes are selected to comprehensively depict the change of battery capacity. Taking the battery capacity as hidden state, an online estimation model is proposed based on approximate belief rule base and hidden Markov model by using historical data and expert knowledge. Based on the performance test data of a certain type of satellite battery, the effectiveness of the proposed model is verified. The capacity of an in-orbit satellite battery is estimated and analyzed by using the telemetry data.

Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK

The Indian Regional Navigation Satellite System (IRNSS) currently can provide independent positioning services with eight in-orbit satellites. This study provides a comprehensive assessment of IRNSS-only data processing, including the availability of satellite constellation, the performance of single-frequency single point positioning (SPP), and the performance of single-frequency short-baseline real-time kinematic (RTK) positioning. Regarding the availability of IRNSS-only case in its primary service areas, the average number of visible satellites is 6–8, and the average Position Dilution of Precision (PDOP) value falls between 3.3 and 6.2, under a service rate of nearly 100.0%. The datasets from 14 stations located in the IRNSS service areas spanning a week are used for position determination. The results show that under the IRNSS single-system case, the positioning accuracy of the SPP is 6.031, 6.015, and 9.668 m in the east, north, and up directions, respectively, and the mean positioning bias of short-baseline RTK is 5.4, −21.1, and −0.2 mm with a standard deviation (STD) error of 7.8, 19.2, and 29.0 mm in the three directions, respectively. For comparative analysis, the results of the GPS single-system and GPS/IRNSS dual-system combination cases are also presented. The positioning performance of IRNSS is inferior to that of GPS, and the performance improvement of GPS/IRNSS dual-system integrated solutions over GPS single-system solutions is not significant. Furthermore, based on the GPS/IRNSS dual-system solutions, the inter-system bias estimates from SPP, the code observation residuals from SPP, and the carrier phase observation residuals from short-baseline RTK are characterized.

Future trends of commercial In- Orbit Satellite Servicing, Active Debris Removal and End-Of Life services

Abstract Space debris growth has become a major threat not only to satellites as well as to the safe operations of the International Space Station (ISS). In September 2021, there were more than 4,700 operational satellites, 36,5000 space debris objects larger than 10 cm and 1 000 000 pieces in the 1cm to 10 cm range. The total mass of all space objects in Earth Orbit is 9,600 tonnes[1]. The launches of Starlink, Amazon Kuiper and OneWeb satellite constellations will increase the threat of space debris collisions. Satellite owners, operators, space agencies and commercial players owning the mega-constellations will need to find economically viable ways to inspect, refuel, augment, extend and manage the lifetime of their satellites. Some of the emerging trends taking place in the space industry are on- orbit satellite servicing, active debris removal services and end-of life services. With the technology demonstrations on orbit of Mission Extension Vehicles (MEV-1), MEV-2 and ELSA-d missions promising In Orbit Satellite (IOS), markets are emerging and expected to reach up to $ 6.2 bln by 2030[2]. The objective of this paper is to identify the role of Bulgarian organisations in the future emerging of in- orbital satellite (IOS) and space situational services markets. Bulgaria’s historical space competencies can contribute to the development of user-driven services/ solutions for space debris inspection, collision avoidance, space robotics and in- orbit servicing and assembly. By encouraging the creation of a new space eco-system in the domain of space debris and space situational awareness in South-Eastern Europe, Bulgarian companies and research organisations are bound to play an important role in the future space robotics markets.

Study of the multi-excitation decoupling optimization method for the integrated structure of a high-resolution optical satellite

In this paper, the sensitive frequency band for distortion and blurring phenomena of the in-orbit satellite is studied and the results are supplements for existing studies and can provide an important reference for the study of micro-vibration. To contain the large-amplitude and micro-vibration excitation response in this sensitive frequency band, a multi-excitation decoupling optimization method is proposed for the integrated structure of high-resolution optical satellites. First, the equivalent dynamics models of the satellite with and without an vibration isolator are established. By deriving the functional relationship between satellite bus stiffness, vibration isolator stiffness, and vibration isolation efficiency, the design objectives can be decoupled and assigned to the vibration isolator and satellite bus in a more cost-effective way, respectively, while maintaining a low computation volume of the multi-objective and multi-variable system optimization. Then, the satellite system is optimized with the objectives of minimizing the large-amplitude vibration response in the launch phase and the micro-vibration response in the orbital operation phase using the NSGA-II genetic algorithm. Finally, the design is verified by ground experiments that it can fully achieve required technical parameters under multiple vibration excitations. This method has certain reference value for the design of complex structural systems of high-resolution satellites.

Microstructure-resolved degradation simulation of lithium-ion batteries in space applications

In-orbit satellite REIMEI, developed by the Japan Aerospace Exploration Agency, has been relying on off-the-shelf Li-ion batteries since its launch in 2005. The performance and durability of Li-ion batteries is impacted by various degradation mechanisms, one of which is the growth of the solid-electrolyte interphase (SEI). In this article, we analyse the REIMEI battery and parameterize a full-cell model with electrochemical cycling data, computer tomography images, and capacity fading experiments using image processing and surrogate optimization. We integrate a recent model for SEI growth into a full-cell model and simulate the degradation of batteries during cycling. To validate our model, we use experimental and in-flight data of the satellite batteries. Our combination of SEI growth model and microstructure-resolved 3D simulation shows, for the first time, experimentally observed inhomogeneities in the SEI thickness throughout the negative electrode for the degraded cells.

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

Similarity based telemetry data recovery for enhancing operating reliability of satellite

Recently, the satellite launching grows rapidly, especially in the commercial aerospace, which produces thousands of in-orbit spacecrafts. The operation and ground management of these satellites is a hotspot issue to ensure the satellites' in-orbit operation reliability. The telemetry data transmission may be delayed by the unstable telemetry link, insufficient ground station sources, interface failures and so on, which exerts a negative influence on the ground management capability and increases the failure risk. Meanwhile, the deep space spacecrafts, space station and Mars exploration expand this transmission delay. Thus, management and intelligent analysis of the telemetry data from Telemetry Ground Station (TGS) gradually become critical for in-orbit satellite operation. To solve above issues, a similarity-based deep learning time series prediction method is proposed to achieve telemetry data recovery. Considering the pseudo-periodic characteristics in telemetry data, this approach extracts similar segments to construct training samples instead of feeding all the observation samples to train the prediction model. As the predicted data is obtained ahead of the actual time-delayed telemetry data, which can provide sufficient analysis time for ground operator to optimize the maintenance strategy. Furthermore, the telemetry prediction data can also guarantee high-complexity digital twin modelling, virtual maintenance and so on. Experimental results with actual satellite telemetry data verify the effectiveness of the proposed method.