Sequential Monte Carlo Filtering with Gaussian Mixture Sampling

No AccessEngineering NotesSequential Monte Carlo Filtering with Gaussian Mixture SamplingSehyun Yun and Renato ZanettiSehyun YunThe University of Texas at Austin, Austin, Texas 78712*Ph.D. Student, Department of Aerospace Engineering and Engineering Mechanics.Search for more papers by this author and Renato ZanettiThe University of Texas at Austin, Austin, Texas 78712†Assistant Professor, Department of Aerospace Engineering and Engineering Mechanics.Search for more papers by this authorPublished Online:17 May 2019https://doi.org/10.2514/1.G004403SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Doucet A., de Freitas N. and Gordon N. J., Sequential Monte Carlo Methods in Practice, Springer, New York, 2001, pp. 3–13. 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R. and Zhao Z., “Measuring Estimator’s Credibility: Noncredibility Index,” Proceedings of the 9th International Information Fusion Conference, Florence, Italy, July 2006, pp. 1–8. doi:https://doi.org/10.1109/ICIF.2006.301770 Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byDegeneracy-Free Particle Filter: Ensemble Kalman Smoother Multiple Distribution Estimation FilterIEEE Transactions on Automatic Control, Vol. 67, No. 12Kernel-based ensemble gaussian mixture filtering for orbit determination with sparse dataAdvances in Space Research, Vol. 69, No. 12Clustering Methods for Particle Filters With Gaussian Mixture ModelsIEEE Transactions on Aerospace and Electronic Systems, Vol. 58, No. 2Bayesian Estimation with Artificial Neural NetworkPlume Source Localization on Enceladus by Sequential Monte Carlo MethodYue Sun, Alex Ellery and Xianlin Huang27 January 2021 | Journal of Spacecraft and Rockets, Vol. 58, No. 4Ensemble Kalman Filter using Gaussian-Sum Predicted State Probability Density FunctionsProceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications, Vol. 2021, No. 0Nonlinear filtering of light-curve dataAdvances in Space Research, Vol. 66, No. 7Extended, Unscented Kalman, and Sigma Point Multiple Distribution Estimation Filters for Nonlinear Discrete State-Space ModelsIEEE Control Systems Letters, Vol. 4, No. 4Simultaneous Estimation of Filtered and Smoothed State Probability Density Functions by Multiple Distribution Estimation What's Popular Volume 42, Number 9September 2019 CrossmarkInformationCopyright © 2019 by Sehyun Yun and Renato Zanetti. 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. TopicsApplied MathematicsComputational Fluid DynamicsFluid DynamicsGeneral PhysicsMathematical AnalysisMonte Carlo MethodNumerical AnalysisRunge-Kutta MethodsStatistical AnalysisStructural Design and DevelopmentStructural Kinematics and DynamicsStructures, Design and Test KeywordsUnscented Kalman FilterMonte Carlo SimulationGaussian Mixture ModelsNumber of ParticlesNonlinear SystemsProbability Density FunctionsDirac Delta FunctionNonlinear DynamicsHeading AngleRunge Kutta IntegrationAcknowledgmentsThis work was sponsored in part by the U.S. Air Force Office of Scientific Research under grant number FA9550-18-1-0351. We would like to thank Mark Psiaki for his help in sharing his simulation and code for the blind tricyclist example.PDF Received19 February 2019Accepted28 March 2019Published online17 May 2019