"[C0FDA04657FD]" . "Unscented Kalman Filter with Controlled Adaptation"@en . . "IFAC Proceedings Volumes (IFAC-PapersOnline)" . . "Straka, Ond\u0159ej" . . . "Nonlinear stochastic system; Kalman filtering; Estimation theory; State estimation"@en . "\u0160imandl, Miroslav" . "RIV/49777513:23520/12:43915568" . "RIV/49777513:23520/12:43915568!RIV13-GA0-23520___" . . . "Unscented Kalman Filter with Controlled Adaptation" . . "P(ED1.1.00/02.0090), P(GAP103/11/1353), S" . . "3"^^ . . "3"^^ . "Unscented Kalman Filter with Controlled Adaptation"@en . "1474-6670" . "16" . . "23520" . "http://dx.doi.org/10.3182/20120711-3-BE-2027.00163" . "176065" . . "Dun\u00EDk, Jind\u0159ich" . "Unscented Kalman Filter with Controlled Adaptation" . . "The paper deals with state estimation of nonlinear stochastic systems with a special focus on the unscented Kalman filter. Recently, several techniques have been proposed to improve estimate quality of the system state by adapting a scaling parameter of the filter. They are, however, tied with an increase of computational costs. To eliminate this drawback a control mechanism is developed in this paper. Its aim is to execute the adaptation only in a case of strongly nonlinear behavior of the measurement function, which is evaluated using two measures of nonlinearity proposed in this paper. To further reduce the computational costs, the paper focuses on a choice of the interval over which the scaling parameter is adapted. The computational costs saving is illustrated in a numerical example." . . . "The paper deals with state estimation of nonlinear stochastic systems with a special focus on the unscented Kalman filter. Recently, several techniques have been proposed to improve estimate quality of the system state by adapting a scaling parameter of the filter. They are, however, tied with an increase of computational costs. To eliminate this drawback a control mechanism is developed in this paper. Its aim is to execute the adaptation only in a case of strongly nonlinear behavior of the measurement function, which is evaluated using two measures of nonlinearity proposed in this paper. To further reduce the computational costs, the paper focuses on a choice of the interval over which the scaling parameter is adapted. The computational costs saving is illustrated in a numerical example."@en . . "6"^^ . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . . "1" . . "10.3182/20120711-3-BE-2027.00163" . . . . .