Titlebook: State Estimation for Nonlinear Continuous–Discrete Stochastic Systems; Numerical Aspects an Gennady Yu. Kulikov,Maria V. Kulikova Book 2024

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2198-4182 to treat stiff, ill-conditioned continuous-time stochastic .This book addresses the problem of accurate state estimation in nonlinear continuous-time stochastic models with additive noise and discrete measurements. Its main focus is on numerical aspects of computation of the expectation and covaria

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Extended Kalman Filtering for Nonlinear Stochastic Modeling Taskscontinuous-time fashion, whereas the measurement ones in use are discrete time. In particular, it gives precise definitions and explains all basic issues and notions of state estimation in nonlinear Gaussian systems of such sort. A special emphasis is placed on two extended Kalman filtering design a

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Unscented Kalman Filtering for Nonlinear Continuous–Discrete Stochastic Systemss precise definitions of the unscented transform and explores its expectation and covariance approximation properties, which create a solid theoretical background for designing advanced state estimation tools in the realm of nonlinear Gaussian systems of such sort. A special emphasis is placed on tw

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Gaussian Filtering for Stiff Continuous–Discrete Stochastic Modeling Tasksastic systems. In particular, it introduces the notion of stiffness in the SDE framework and extends it then to continuous–discrete stochastic state estimation tasks of such sort. Based on the stability analysis of Dahlquist elaborated in Sect. . of Chap. 1, our consideration focuses on stiffness fe