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Autonomous navigation method of satellite constellation based on adaptive forgetting factors

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Chinese Journal of Aeronautics 2024年第7期37卷 317-332页

作者： Dong WANG Jing YANG Kai XIONG School of Automation science and Electrical Engineering Beihang UniversityBeijing 100191China science and Technology on space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100094China

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is *** process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting *** adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more *** intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency *** that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed *** analysis of the proposed method is also *** effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison *** estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

关键词： Constellation autonomous navigation Unscented Kalman filter Adaptive forgetting factor Model uncertainty Stability analysis

Study of factors influencing the occurrence rate of 60 Hz power line radiation in the topside ionosphere:A systematic survey using the CSES satellite

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science China(Technological sciences) 2024年第6期67卷 1879-1892页

作者： MA ZhenHui ZHAO ShuFan LIAO Li SHEN XuHui LU HengXin School of Emergency Management science and Engineering University of Chinese Academy of SciencesBeijing100049China State Key Laboratory of space Weather National Space Science CenterChinese Academy of SciencesBeijing100190China Key Laboratory of Solar Activity and space Weather National Space Science CenterChinese Academy of SciencesBeijing100190China National institute of Natural Hazards Ministry of Emergency Management of ChinaBeijing100085China institute of Geophysics China Earthquake AdministrationBeijing100081China

We analyze the topside ionosphere power line radiation(PLR)at 60 Hz over the US using electric field data collected by CSES satellite between January 2019 and December *** study aimed to further investigate the month-to-month variation characteristic of PLR occurrence rate observed by satellite and its several influencing factors,including solar radiation,lightning activity,and try to clarify the influence of electricity *** results show that the solar radiation(solar zenith angle and F10.7)plays a major role in the variation of the PLR occurrence rate,and that there is no direct connection with the number of *** the relationship between PLR occurrence rate and electricity consumption,the low occurrence rate associated with decreased weekend electricity consumption was not observed in the ***,there is a significant difference in PLR occurrence rate between the East and West Coasts of the US at the same latitude during the same time period,suggesting that the significant difference in PLR occurrence rate is caused by the significant difference in electricity consumption between the two *** excluding the effect of solar radiation on PLR occurrence rate,we concluded that only a significant difference in regional electricity consumption could lead to a corresponding change in PLR occurrence rate detected by the Low-Earth-Orbit ***,we also found there is seasonal variation in the diurnal differences of the PLR occurrence rate caused by seasonal variation of the lower ionosphere.

关键词： CSES satellite power line radiation occurrence rate solar radiation lightning activity electricity consumption

GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

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Digital Communications and Networks 2024年第3期10卷 620-630页

作者： Guanlin Wu Haipeng Wang Yu Liu You He Shenzhen international Graduate School Tsinghua UniversityShenzhen518071China institute of space science and Applied Technology Harbin Institute of TechnologyShenzhen518055China Department of Electronic Engineering Tsinghua UniversityBeijing100084China

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in *** unmanned MMS,one of the key technologies is situation ***,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation *** order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big ***,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and *** experiments show the effectiveness and robustness of the proposed method.

关键词： Internet of things Multi-agents Graph neural network Maritime monitoring services

QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO School of Computer science and Technology Beijing Institute of Technology School of space Information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Training Streams Random forests

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

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Defence Technology（防务技术） 2024年第2期32卷 271-284页

作者： Yiwen Zhang Wei Zheng Zongqiang Liu Qian Xuesen Laboratory of space Technology China Academy of Space TechnologyBeijing 100094China China Academy of Aerospace science and Innovation Beijing 100176China State Key Laboratory of space-Ground Integrated Information Technology Beijing Institute of Satellite Information EngineeringBeijing 100194China

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater *** present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically ***,GNSS-R satellite constellations that meet the global altimetry needs to be ***,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric ***,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is ***,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is ***,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

关键词： GNSS-R satellite constellations Sea surface altimetric precision Underwater navigation Multilayer feedforward neural network

Continuous preparation of strong and tough PVA nanocomposite fibers by mechanical stretching-assisted salting-out treatment

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Nano Research 2024年第4期17卷 3156-3163页

作者： Hang Chen Guangze Yang Yingzhi Sun Yichao Xu Mingjie Liu Key Laboratory of Bioinspired Smart Interfacial science and Technology of Ministry of Education School of ChemistryBeihang UniversityBeijing 100191China Research institute for Frontier science Beihang UniversityBeijing 100191China international Research institute for Multidisciplinary science Beihang UniversityBeijing 100191China

Polymer composite fibers with superior properties such as excellent combined strength and toughness and biocompatibility can be used in high-tech applications of braided protective devices and smart wearable,however the research of high-performance polymer composite fiber remains in the infant *** we present a strategy to produce strong and tough anisotropic polymer nanocomposite fibers with orientedly aligned salt rods using mechanical stretching-assisted salting-out *** prepared nanocomposite fibers have a tensile strength of up to 786±2.7 MPa and an elongation at break of 81%,and the anisotropic fibers exhibit good transmission of mechanical vibration in the longitudinal direction with high *** the fabrication process,the salt builds up into oriented rods during the directional salting process,and the polymer is confined to the 150 nm domain between the rods after the solvent is completely evaporated,giving the nanocomposite fibers superior mechanical *** presented strategy can be applied to the continuous mass production of nanocomposite fibers and is also generalizable to other polymer nanocomposites,which could extend the applicability of nanocomposite fibers to conditions involving more demanding mechanical loading and mechanical vibration transmission.

关键词： poly(vinyl alcohol)(PVA)nanofibers oriented structure ultrastrong and stiff mechanical vibration transmission salting out continuous production

Joint observations of the large-scale ULF wave activity from space to ground associated with the solar wind dynamic pressure enhancement

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science China(Technological sciences) 2024年第7期67卷 2215-2229页

作者： SUN XiaoYing HU YunPeng ZHIMA Zeren DUAN SuPing LV FangXian SHEN XuHui National institute of Natural Hazards Ministry of Emergency ManagementBeijing 100085China School of space and Environment Beihang UniversityBeijing 102206China State Key Laboratory of space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China

This study reports the rare ultralow-frequency(ULF) wave activity associated with the solar wind dynamic pressure enhancement that was successively observed by the GOES-17(Geostationary Operational Environmental Satellite) in the magnetosphere, the CSES(China Seismo-Electromagnetic Satellite) in the ionosphere, and the THEMIS ground-based observatories(GBO) GAKO and EAGL in the Earth's polar region during the main phase of an intense storm on 4 November 2021. Along with the enhanced-pressure solar wind moving tailward, the geomagnetic field structure experienced a large-scale change. From dawn/dusk sides to midnight, the GAKO, EAGL, and GOES-17 sequentially observed the ULF waves in a frequency range of0.04–0.36 Hz at L shells of ~5.07, 6.29, and 5.67, respectively. CSES also observed the ULF wave event with the same frequency ranges at wide L-shells of 2.52–6.22 in the nightside ionosphere. The analysis results show that the ULF waves at ionospheric altitude were mixed toroidal-poloidal mode waves. Comparing the ULF waves observed in different regions, we infer that the nightside ULF waves were directly or indirectly excited by the solar wind dynamic pressure increase: in the area of L-shells~2.52–6.29, the magnetic field line resonances(FLRs) driven by the solar wind dynamic pressure increase is an essential excitation source;on the other hand, around L~3.29, the ULF waves can also be excited by the outward expansion of the plasmapause owing to the decrease of the magnetospheric convection, and in the region of L-shells ~5.19–6.29, the ULF waves are also likely excited by the ion cyclotron instabilities driven by the solar wind dynamic pressure increase.

关键词： ultralow-frequency(ULF)wave solar wind dynamic pressure(P_(sw)) magnetic field line resonance(FLR) ion cyclotron instability

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Autonomous Recommendation of Fault Detection Algorithms for spacecraft

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 273-275页

作者： Wenbo Li Baoling Ning the National Key Laboratory of science and Technology on space Intelligent Control Beijing Institute of Control EngineeringBeijing 100094China the School of Data science and Technology Heilongjiang UniversityHarbin 150001China

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

关键词： fault introducing letter

Understanding the Low Predictability of the 2015/16 El Niño Event Based on a Deep Learning Model

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Advances in Atmospheric sciences 2024年第7期41卷 1313-1325页

作者： Tingyu WANG Ping HUANG Xianke YANG Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National space science Center Chinese Academy of SciencesBeijing 100190China State Key Laboratory of space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China College of Plant science and Technology Huazhong Agricultural UniversityWuhan 430070China

The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer ***,the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them *** present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event.A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 *** integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 *** crucial signals are then masked in the initial conditions to verify their roles in the *** addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies,we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event,emphasizing the crucial role of the interactions among them and the North *** approach is also applied to other El Niño events,revealing several new precursor *** study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.

关键词： ENSO attribution deep learning ENSO prediction extreme El Niño