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- W2122246449 abstract "In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix Q by solving an optimization problem over a short window of data. The algorithm recovers the observations h(x) from a system x@?=f(x),y=h(x)+v without a priori knowledge of system dynamics. Potential applications include target tracking using a network of nonlinear sensors, servoing, mapping, and localization. The algorithm is demonstrated in simulations on a tracking example for a target with coupled and nonlinear kinematics. Simulations indicate superiority over a standard MMAE algorithm for a large class of systems." @default.
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- W2122246449 date "2011-08-01" @default.
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- W2122246449 title "An optimization approach to adaptive Kalman filtering" @default.
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- W2122246449 doi "https://doi.org/10.1016/j.automatica.2011.04.004" @default.
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