Analysis identifies reduced order extended Kalman filter outperforms full-order version in nonlinear dynamic systems, indicating improved accuracy.
This work introduces a new reduced order extended Kalman filter (REKF) for discrete nonlinear dynamic systems which is derived by least square minimization of the local estimation error covariance. Both the filter and the 1-step ahead predictor forms are derived. For demonstration purposes, the REKF is applied to a polynomial nonlinear system, and its estimation error is compared to that of the full-order EKF.
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Strandt et al. (2025) studied this question.
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