Dynamic-Covariance-Scaling-Based Robust Sigma-Point Information Filtering

No AccessEngineering NotesDynamic-Covariance-Scaling-Based Robust Sigma-Point Information FilteringShoupeng Li, Naigang Cui and Rongjun MuShoupeng Li https://orcid.org/0000-0001-8431-2789Harbin Institute of Technology, 150001 Harbin, People’s Republic of China*Ph.D. Candidate, School of Astronautics, Heilongjiang; .Search for more papers by this author, Naigang CuiHarbin Institute of Technology, 150001 Harbin, People’s Republic of China†Professor, School of Astronautics, Heilongjiang; .Search for more papers by this author and Rongjun MuHarbin Institute of Technology, 150001 Harbin, People’s Republic of China‡Professor, School of Astronautics, Heilongjiang; .Search for more papers by this authorPublished Online:1 Jun 2021https://doi.org/10.2514/1.G005881SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Van Der Merwe R., “Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models,” Ph.D. Thesis, OGI School of Science and Engineering, Oregon Health and Science Univ., Portland, OR, 2004. 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TopicsAlgorithms and Data StructuresApplied MathematicsArtificial IntelligenceAviation CommunicationCommunication SystemComputing, Information, and CommunicationData ScienceEarth SciencesGeneral PhysicsGeophysicsMachine LearningMathematical AnalysisOptimization AlgorithmRADARStatistical Analysis KeywordsMonte Carlo SimulationBallistic CoefficientRADARKernel FunctionsKalman FilterProbability Density FunctionsNumerical SimulationVehicle TrajectoriesNonlinear SystemsMaximum Likelihood EstimationPDF Received29 December 2020Accepted12 April 2021Published online1 June 2021