FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4310277245> ?p ?o ?g. }

• W4310277245 endingPage "11" @default.
• W4310277245 startingPage "1" @default.
• W4310277245 abstract "No AccessEngineering NotesHybrid Gaussian Mixture Splitting Techniques for Uncertainty Propagation in Nonlinear DynamicsPan Sun, Camilla Colombo, Mirko Trisolini and Shuang LiPan Sun https://orcid.org/0000-0001-6182-4503Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, People’s Republic of China*Ph.D. Candidate, Advanced Space Technology Laboratory, and College of Astronautics; .Search for more papers by this author, Camilla Colombo https://orcid.org/0000-0001-9636-9360Polytechnic University of Milan, 20156 Milan, Italy†Associate Professor, Department of Aerospace Science and Technology; .Search for more papers by this author, Mirko Trisolini https://orcid.org/0000-0001-9552-3565Polytechnic University of Milan, 20156 Milan, Italy‡Postdoc Research Fellow, Department of Aerospace Science and Technology; .Search for more papers by this author and Shuang Li https://orcid.org/0000-0001-9142-5036Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, People’s Republic of China§Professor, Advanced Space Technology Laboratory, and College of Astronautics; .Search for more papers by this authorPublished Online:27 Nov 2022https://doi.org/10.2514/1.G006696SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Psiaki M. L., Weisman R. M. and Jah M. K., “Gaussian Mixture Approximation of Angles-Only Initial Orbit Determination Likelihood Function,” Journal of Guidance, Control, and Dynamics, Vol. 40, No. 11, 2017, pp. 2807–2819. https://doi.org/10.2514/1.G002615 LinkGoogle Scholar[2] Vittaldev V. and Russell R. P., “Space Object Collision Probability Using Multidirectional Gaussian Mixture Models,” Journal of Guidance, Control, and Dynamics, Vol. 39, No. 9, 2016, pp. 2163–2169. https://doi.org/10.2514/1.G001610 LinkGoogle Scholar[3] Trisolini M. and Colombo C., “Propagation and Reconstruction of Reentry Uncertainties Using Continuity Equation and Simplicial Interpolation,” Journal of Guidance, Control, and Dynamics, Vol. 44, No. 4, 2021, pp. 793–811. https://doi.org/10.2514/1.G005228 LinkGoogle Scholar[4] Yang Z., Luo Y. Z., Lappas V. and Tsourdos A., “Nonlinear Analytical Uncertainty Propagation for Relative Motion Near J2-Perturbed Elliptic Orbits,” Journal of Guidance, Control, and Dynamics, Vol. 41, No. 4, 2018, pp. 888–903. https://doi.org/10.2514/1.G003071 LinkGoogle Scholar[5] Luo Y. Z. and Yang Z., “A Review of Uncertainty Propagation in Orbital Mechanics,” Progress in Aerospace Sciences, Vol. 89, Feb. 2017, pp. 23–39. https://doi.org/10.1016/j.paerosci.2016.12.002 CrossrefGoogle Scholar[6] Julier S. J. and Uhlmann J. K., “Unscented Filtering and Nonlinear Estimation,” Proceedings of the IEEE, Vol. 92, No. 3, March 2004, pp. 401–422. https://doi.org/10.1109/JPROC.2003.823141 CrossrefGoogle Scholar[7] Park R. S. and Scheeres D. J., “Nonlinear Semi-Analytic Methods for Trajectory Estimation,” Journal of guidance, Control, and Dynamics, Vol. 30, No. 6, 2007, pp. 1668–1676. https://doi.org/10.2514/1.29106 LinkGoogle Scholar[8] Wittig A., Colombo C. and Armellin R., “Long-Term Density Evolution Through Semi-Analytical and Differential Algebra Techniques,” Celestial Mechanics and Dynamical Astronomy, Vol. 128, No. 4, 2017, pp. 435–452. https://doi.org/10.1007/s10569-017-9756-x CrossrefGoogle Scholar[9] Alspach D. and Sorenson H., “Nonlinear Bayesian Estimation Using Gaussian Sum Approximations,” IEEE transactions on automatic control, Vol. 17, No. 4, 1972, pp. 439–448. https://doi.org/10.1109/TAC.1972.1100034 CrossrefGoogle Scholar[10] Giza D., Singla P. and Jah M., “An Approach for Nonlinear Uncertainty Propagation: Application to Orbital Mechanics,” AIAA Guidance, Navigation, and Control Conference, AIAA Paper 2009-6082, Aug. 2009. https://doi.org/10.2514/6.2009-6082 Google Scholar[11] Tenne D. and Singh T., “The Higher Order Unscented Filter,” Proceedings of the 2003 American Control Conference, Vol. 3, IEEE Publ., Piscataway, NJ, June 2003, pp. 2441–2446. https://doi.org/10.1109/ACC.2003.1243441 Google Scholar[12] Younes A. B., Turner J., Majji M. and Junkins J., “High-Order Uncertainty Propagation Using State Transition Tensor Series,” Advances in the Astronautical Sciences, AAS Paper 12-636, Jan. 2013. Google Scholar[13] Wittig A., Di Lizia P., Armellin R., Makino K., Bernelli-Zazzera F. and Berz M., “Propagation of Large Uncertainty Sets in Orbital Dynamics by Automatic Domain Splitting,” Celestial Mechanics and Dynamical Astronomy, Vol. 122, No. 3, 2015, pp. 239–261. https://doi.org/10.1007/s10569-015-9618-3 CrossrefGoogle Scholar[14] DeMars K. J., Bishop R. H. and Jah M. K., “Entropy-Based Approach for Uncertainty Propagation of Nonlinear Dynamical Systems,” Journal of Guidance, Control, and Dynamics, Vol. 36, No. 4, 2013, pp. 1047–1057. https://doi.org/10.2514/1.58987 LinkGoogle Scholar[15] Terejanu G., Singla P., Singh T. and Scott P. D., “Uncertainty Propagation for Nonlinear Dynamic Systems Using Gaussian Mixture Models,” Journal of Guidance, Control, and Dynamics, Vol. 31, No. 6, 2013, pp. 1623–1633. https://doi.org/10.2514/1.36247 Google Scholar[16] Vishwajeet K., Singla P. and Jah M., “Nonlinear Uncertainty Propagation for Perturbed Two-Body Orbits,” Journal of Guidance, Control, and Dynamics, Vol. 37, No. 5, 2014, pp. 1415–1425. https://doi.org/10.2514/1.G000472 LinkGoogle Scholar[17] Vishwajeet K. and Singla P., “Adaptive Split/Merge-Based Gaussian Mixture Model Approach for Uncertainty Propagation,” Journal of Guidance, Control, and Dynamics, Vol. 41, No. 3, 2017, pp. 603–617. https://doi.org/10.2514/1.G002801 Google Scholar[18] Tuggle K. and Zanetti R., “Automated Splitting Gaussian Mixture Nonlinear Measurement Update,” Journal of Guidance, Control, and Dynamics, Vol. 41, No. 3, 2018, pp. 725–734. https://doi.org/10.2514/1.G003109 LinkGoogle Scholar[19] Krivov A. V. and Getino J., “Orbital Evolution of High-Altitude Balloon Satellites,” Astronomy and Astrophysics, Vol. 318, Feb. 1997, pp. 308–314. Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byAdaptive Gaussian Mixture Model for Uncertainty Propagation Using Virtual Sample Generation27 February 2023 | Applied Sciences, Vol. 13, No. 5 What's Popular Volume 46, Number 4April 2023 CrossmarkInformationCopyright © 2022 by the authors. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-3884 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerospace SciencesArtificial IntelligenceAstrodynamicsAstronauticsComputing, Information, and CommunicationData ScienceEntropyMachine LearningOrbital ManeuversSpace OrbitStructural Design and DevelopmentStructural Kinematics and DynamicsStructures, Design and TestThermodynamic PropertiesThermodynamicsThermophysics and Heat Transfer KeywordsStructural Kinematics and DynamicsEntropyGaussian Mixture ModelsMedium Earth OrbitOrbital MechanicsPlanetsProbability Density FunctionsOrbit DeterminationSolar RadiationHydrodynamicsAcknowledgmentsThis research work has received funding from the National Natural Science Foundation of China (Grant Nos. 11972182 and 11672126), Qing Lan Project, China Scholarship Council (CSC No. 202006830123), and the 2020 Postgraduate Research Practice Innovation Program of Jiangsu Province (Grant No. KYCX20_0222). C. Colombo and M. Trisolini acknowledge the COMPASS project, which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 679086—COMPASS). Pan Sun acknowledges the anonymous reviewers for the constructive comments for improving the paper.PDF Received19 January 2022Accepted9 October 2022Published online27 November 2022" @default.
• W4310277245 created "2022-11-30" @default.
• W4310277245 creator A5015237276 @default.
• W4310277245 creator A5061084605 @default.
• W4310277245 creator A5062668990 @default.
• W4310277245 creator A5082641726 @default.
• W4310277245 date "2022-11-27" @default.
• W4310277245 modified "2023-10-14" @default.
• W4310277245 title "Hybrid Gaussian Mixture Splitting Techniques for Uncertainty Propagation in Nonlinear Dynamics" @default.
• W4310277245 cites W1954431576 @default.
• W4310277245 cites W1981936413 @default.
• W4310277245 cites W2069146315 @default.
• W4310277245 cites W2090476902 @default.
• W4310277245 cites W2099867508 @default.
• W4310277245 cites W2117914019 @default.
• W4310277245 cites W2123487311 @default.
• W4310277245 cites W2467122427 @default.
• W4310277245 cites W2561192192 @default.
• W4310277245 cites W2593360758 @default.
• W4310277245 cites W2736460447 @default.
• W4310277245 cites W2766680514 @default.
• W4310277245 cites W2767147389 @default.
• W4310277245 cites W2773069665 @default.
• W4310277245 cites W3121583687 @default.
• W4310277245 doi "https://doi.org/10.2514/1.g006696" @default.
• W4310277245 hasPublicationYear "2022" @default.
• W4310277245 type Work @default.
• W4310277245 citedByCount "2" @default.
• W4310277245 countsByYear W43102772452023 @default.
• W4310277245 crossrefType "journal-article" @default.
• W4310277245 hasAuthorship W4310277245A5015237276 @default.
• W4310277245 hasAuthorship W4310277245A5061084605 @default.
• W4310277245 hasAuthorship W4310277245A5062668990 @default.
• W4310277245 hasAuthorship W4310277245A5082641726 @default.
• W4310277245 hasConcept C121332964 @default.
• W4310277245 hasConcept C127413603 @default.
• W4310277245 hasConcept C146978453 @default.
• W4310277245 hasConcept C161191863 @default.
• W4310277245 hasConcept C163716315 @default.
• W4310277245 hasConcept C166957645 @default.
• W4310277245 hasConcept C167740415 @default.
• W4310277245 hasConcept C178802073 @default.
• W4310277245 hasConcept C191935318 @default.
• W4310277245 hasConcept C205649164 @default.
• W4310277245 hasConcept C33923547 @default.
• W4310277245 hasConcept C41008148 @default.
• W4310277245 hasConcept C60808876 @default.
• W4310277245 hasConcept C62520636 @default.
• W4310277245 hasConceptScore W4310277245C121332964 @default.
• W4310277245 hasConceptScore W4310277245C127413603 @default.
• W4310277245 hasConceptScore W4310277245C146978453 @default.
• W4310277245 hasConceptScore W4310277245C161191863 @default.
• W4310277245 hasConceptScore W4310277245C163716315 @default.
• W4310277245 hasConceptScore W4310277245C166957645 @default.
• W4310277245 hasConceptScore W4310277245C167740415 @default.
• W4310277245 hasConceptScore W4310277245C178802073 @default.
• W4310277245 hasConceptScore W4310277245C191935318 @default.
• W4310277245 hasConceptScore W4310277245C205649164 @default.
• W4310277245 hasConceptScore W4310277245C33923547 @default.
• W4310277245 hasConceptScore W4310277245C41008148 @default.
• W4310277245 hasConceptScore W4310277245C60808876 @default.
• W4310277245 hasConceptScore W4310277245C62520636 @default.
• W4310277245 hasFunder F4320321001 @default.
• W4310277245 hasFunder F4320338335 @default.
• W4310277245 hasLocation W43102772451 @default.
• W4310277245 hasOpenAccess W4310277245 @default.
• W4310277245 hasPrimaryLocation W43102772451 @default.
• W4310277245 hasRelatedWork W2331540041 @default.
• W4310277245 hasRelatedWork W2905250601 @default.
• W4310277245 hasRelatedWork W2914779019 @default.
• W4310277245 hasRelatedWork W291591453 @default.
• W4310277245 hasRelatedWork W2959992216 @default.
• W4310277245 hasRelatedWork W3044511468 @default.
• W4310277245 hasRelatedWork W3157659943 @default.
• W4310277245 hasRelatedWork W3173943080 @default.
• W4310277245 hasRelatedWork W3194389241 @default.
• W4310277245 hasRelatedWork W2576749245 @default.
• W4310277245 isParatext "false" @default.
• W4310277245 isRetracted "false" @default.
• W4310277245 workType "article" @default.