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- W4328008189 abstract "This chapter presents the algorithm for real-time updating of the noise covariance matrices in the extended Kalman filter. This content is motivated from practical applications, in which the noise statistics of the Kalman filter or its variants are usually not known a priori. To address this issue, a Bayesian probabilistic algorithm is developed to estimate the noise parameters which are utilized to parameterize the noise covariance matrices in the extended Kalman filter. A computationally efficient algorithm is then introduced to resolve the optimization problem formulated by using the Bayesian inference. The proposed method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in a real-time manner. This method does not impose any stationarity condition of the process noise and measurement noise. By removing the stationarity constraint in the extended Kalman filter, the proposed method enhances the applicability of the real-time system identification algorithm for nonstationary circumstances generally encountered in practice. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are presented to illustrate the practical aspects in real-time system identification." @default.
- W4328008189 created "2023-03-22" @default.
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- W4328008189 date "2023-01-01" @default.
- W4328008189 modified "2023-10-01" @default.
- W4328008189 title "Real-Time Updating of Noise Parameters for System Identification" @default.
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- W4328008189 doi "https://doi.org/10.1007/978-981-99-0593-5_3" @default.
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