Results 281 to 290 of about 26,219 (311)
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Kalman filter and quantization
Problems of Information Transmission, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2007 IEEE 22nd International Symposium on Intelligent Control, 2007
This paper is concerned with the estimation problem for a dynamic stochastic estimation in a sensor network. Firstly, the quantized Kalman filter based on the quantized observations (QKFQO) is presented. Approximate solutions for two optimal bandwidth scheduling problems are given, where the tradeoff between the number of quantization levels or the ...
Shuli Sun +3 more
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This paper is concerned with the estimation problem for a dynamic stochastic estimation in a sensor network. Firstly, the quantized Kalman filter based on the quantized observations (QKFQO) is presented. Approximate solutions for two optimal bandwidth scheduling problems are given, where the tradeoff between the number of quantization levels or the ...
Shuli Sun +3 more
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Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
Infinite-horizon Kalman filtering is re-examined and generalized to include a class of nonstationary and nonergodic disturbances. This revision is achieved by defining a generalized infinite-horizon filtering problem using a flexible functional analytic signal description. It is shown that the solution to the generalized filtering problem is equivalent
Pertti M. Makila, J. Paattilammi
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Infinite-horizon Kalman filtering is re-examined and generalized to include a class of nonstationary and nonergodic disturbances. This revision is achieved by defining a generalized infinite-horizon filtering problem using a flexible functional analytic signal description. It is shown that the solution to the generalized filtering problem is equivalent
Pertti M. Makila, J. Paattilammi
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IEEE Transactions on Signal Processing, 1994
A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations. >
Simon Haykin 0001, Liang Li
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A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations. >
Simon Haykin 0001, Liang Li
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2017
Damit Kalman-Filter korrekt eingesetzt werden konnen, ist es wichtig, die Randbedingen zu kennen, unter der die Kalman-Gleichungen verwendet werden durfen. Dies bedeutet, dass die jeweilig zu losende Aufgabe dahingehend zu uberprufen ist. Sind diese Voraussetzungen nicht gegeben, liefern die Kalman-Gleichungen nicht das gewunschte Ergebnis.
Reiner Marchthaler, Sebastian Dingler
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Damit Kalman-Filter korrekt eingesetzt werden konnen, ist es wichtig, die Randbedingen zu kennen, unter der die Kalman-Gleichungen verwendet werden durfen. Dies bedeutet, dass die jeweilig zu losende Aufgabe dahingehend zu uberprufen ist. Sind diese Voraussetzungen nicht gegeben, liefern die Kalman-Gleichungen nicht das gewunschte Ergebnis.
Reiner Marchthaler, Sebastian Dingler
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State-Space Model and Kalman Filter Gain Identification by a Kalman Filter of a Kalman Filter
Journal of Dynamic Systems, Measurement, and Control, 2017This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input–output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter.
Minh Q. Phan +3 more
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Kalman filter and extended Kalman filter
2017In the Bachelor’s thesis we describe the Kalman filtering algorithm for linear-Gaussian state space models and give an example of its application. We describe the extended Kalman filter for differentiable Gaussian state space models and give examples of its application.
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Automatica
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xusheng Yang +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xusheng Yang +2 more
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Adaptive Kalman Filter (ROSE Filter)
2017Um das Kalman-Filter optimal zu nutzen, ist es von groserWichtigkeit, die Kovarianz des Messrauschens \( \underline{R} \left( k \right) \) und des Systemrauschen \( \underline{Q} \left( k \right) \) moglichst exakt zu bestimmen. Erst durch eine exakte Bestimmung der beiden Kovarianzen ist es moglich, eine optimale Zustandsschatzung und eine korrekte ...
Reiner Marchthaler, Sebastian Dingler
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2008
The Kalman and particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the particle filter does so by a sequential Monte Carlo method.
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The Kalman and particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the particle filter does so by a sequential Monte Carlo method.
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