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- W1976407211 abstract "This paper presents a method of designing a suboptimal Kalman filter (SKF) for nonlinear systems, including uncertainties on the system parameter dynamics. In general, governing dynamics behind real-world phenomena has significant associated uncertainties and all we can presume is the approximation to system models. We thus need a robust filter that is capable of rapidly following up the unknown system model parameters while adequately estimating the system states as well. The SKF is suboptimal in terms of standard square errors that the KF minimizes at each time step and this suboptimization results in the enhancement of filter's parameter adaptation ability. The nonlinearity of system models is handled by using an unscented transforming statistical linearization without losing the Gaussianity of states to be estimated. We confirm the effectiveness of the proposed filter by numerical simulations." @default.
- W1976407211 created "2016-06-24" @default.
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- W1976407211 date "2014-01-01" @default.
- W1976407211 modified "2023-09-26" @default.
- W1976407211 title "Suboptimal Kalman Filter for Dual Estimation under Dynamical Uncertainties" @default.
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- W1976407211 doi "https://doi.org/10.3182/20140824-6-za-1003.00336" @default.
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