Titlebook: Nonlinear Filtering; Methods and Applicat Kumar Pakki Bharani Chandra,Da-Wei Gu Book 2019 Springer Nature Switzerland AG 2019 Advanced Stat

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Variants of Cubature Kalman Filter,apter, variants of CKF, namely the cubature information filter (CIF), cubature . filter (C.F) and cubature . information filter (C.IF), and their square-root versions, will be explored. Each of these filters is suitable for particular applications. For example, the CIF is suitable for state estimati

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More Estimation Methods and Beyond,bserver. Though the UKF filter and SDRE observer are based on different philosophies, both of them are derivative-free, nonlinear, state estimators. Their formulations and applications to nonlinear systems are described in this chapter with illustrative application examples. Towards the end of this

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Jacobian-Based Nonlinear State Estimation,ement dynamics. The idea is, in some key steps of estimation, to replace the nonlinear process and/or measurement models with their corresponding Jacobians. The three filters to be discussed in this chapter are therefore the extended Kalman filter (EKF), extended information filter (EIF) and extended . filter (E.F).

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More Estimation Methods and Beyond,observation data missing occurring in the state estimation will be discussed. The method of linear prediction (LP) is particularly introduced to alleviate the adverse effect of data missing in the state estimation, together with a numerical example.

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