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ndltd-NEU--neu-805
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oai_dc
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ndltd-NEU--neu-8052021-05-26T05:10:41ZApplied Kalman filter theoryThe objective of this study is to examine three problems that arise in experimental mechanics where Kalman filter (KF) theory is used. The first is estimating the steady state KF gain from measurements in the absence of process and measurement noise statistics. In an off-line setting the estimation of noise covariance matrices, and the associated filter gain from measurements is theoretically feasible but lead to an ill-conditioned linear least square problem. In this work the merit of Tikhonov's regularization is examined in order to improve the poor estimates of the noise covariance matrices and steady state Kalman gain.http://hdl.handle.net/2047/d20003550
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NDLTD
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NDLTD
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| description |
The objective of this study is to examine three problems that arise in experimental mechanics where Kalman filter (KF) theory is used. The first is estimating the steady state KF gain from measurements in the absence of process and measurement noise statistics. In an off-line setting the estimation of noise covariance matrices, and the associated filter gain from measurements is theoretically feasible but lead to an ill-conditioned linear least square problem. In this work
the merit of Tikhonov's regularization is examined in order to improve the poor estimates of the noise covariance matrices and steady state Kalman gain.
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| title |
Applied Kalman filter theory
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| spellingShingle |
Applied Kalman filter theory
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| title_short |
Applied Kalman filter theory
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| title_full |
Applied Kalman filter theory
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| title_fullStr |
Applied Kalman filter theory
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| title_full_unstemmed |
Applied Kalman filter theory
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| title_sort |
applied kalman filter theory
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| url |
http://hdl.handle.net/2047/d20003550
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| _version_ |
1719406429175021568
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