Results 91 to 100 of about 64,413 (127)
Advanced algorithms for UAV tracking of targets exhibiting start-stop and irregular motion. [PDF]
Sci RepNishad DK +4 more
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Predictive signal modeling and multi-rate filtering in accelerated cardiac MRI. [PDF]
Magn Reson MedCurtis AD +6 more
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Multi-Sensor Monitoring, Intelligent Control, and Data Processing for Smart Greenhouse Environment Management. [PDF]
Sensors (Basel)Bicamumakuba E +5 more
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International Journal of Control, 1997
For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit
M, Skliar, W F, Ramirez
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For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit
M, Skliar, W F, Ramirez
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Asian Journal of Control, 2016
AbstractIn the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed.
Mohammaddadi, Gh. +2 more
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AbstractIn the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed.
Mohammaddadi, Gh. +2 more
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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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Adaptiver 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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