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- W4328008059 abstract "This chapter presents the standard Kalman filter (KF) and the extended Kalman filter (EKF). The detailed derivations of the discrete-time KF and EKF are presented from a Bayesian perspective. In order to formulate the KF algorithm, the state space model of a linear dynamical system is introduced. By using the Bayes’ theorem, the conditional probability density function for prediction can be obtained in a recursive manner and the analytical solutions can be obtained. Applications for a vehicle tracking system and a structural state estimation problem are provided to demonstrate the state tracking capability of the KF algorithm. Afterwards, the nonlinear state space model for general nonlinear dynamical systems is introduced and it will be utilized in the EKF. By expanding the nonlinear equations using the Taylor series expansion, the locally linearized state space model can be obtained and the procedures of the EKF algorithm are formulated in the same manner as the standard KF algorithm. The EKF with fading memory is introduced to enhance the tracking capability for time-varying systems. Applications to simultaneous states and model parameters estimation are presented. The KF algorithm and its variants provide an effective and efficient way for recursive estimation. More importantly, the EKF is the fundamental algorithm in this book." @default.
- W4328008059 created "2023-03-22" @default.
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- W4328008059 date "2023-01-01" @default.
- W4328008059 modified "2023-09-29" @default.
- W4328008059 title "System Identification Using Kalman Filter and Extended Kalman Filter" @default.
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- W4328008059 doi "https://doi.org/10.1007/978-981-99-0593-5_2" @default.
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