文件名称:fcn_SR_KF
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This file compares three different versions of the Kalman filter.
The Kalman filter is used for recursive parameter estimation.
The Kalman filter can handle noisy measurements.
The first implemented filter (fcn_KF) is the Kalman filter with standard
update of the covariance matrix P.
The covariance matrix reflects the uncertainties of the predictions.
To improve the numerical stability Potter developed a
square root update (fcn_KF_SRP) of the covariance matrix P.
Another version is the square root covariance update via
triangularization (fcn_KF_SRT).
This file generates a model. Then the three Kalman filters perform an
estimation of the model parameter. At the end the results are compared.
Sources:
Simon, D. (2006): Optimal state estimation
Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques
Golub, G. (1996): Matrix Computations
The Kalman filter is used for recursive parameter estimation.
The Kalman filter can handle noisy measurements.
The first implemented filter (fcn_KF) is the Kalman filter with standard
update of the covariance matrix P.
The covariance matrix reflects the uncertainties of the predictions.
To improve the numerical stability Potter developed a
square root update (fcn_KF_SRP) of the covariance matrix P.
Another version is the square root covariance update via
triangularization (fcn_KF_SRT).
This file generates a model. Then the three Kalman filters perform an
estimation of the model parameter. At the end the results are compared.
Sources:
Simon, D. (2006): Optimal state estimation
Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques
Golub, G. (1996): Matrix Computations
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下载文件列表
fcn_SR_KF.m
license.txt