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AnadaptiveKalmanfilterfordynamicharmonicstateestim
... noise covariance matrix
is essential for the application of Kalman filtering. However,
it is usually a difficult task to ... time varying systems. This paper looks at an adaptive
Kalman filter method for dynamic harmonic state estimation and
harmonic ...
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AnadaptiveKalmanfilterfortheenhancementofspeechsig
... of speech enhancement when a
corrupted speech signal with an additive colored noise is the only
information available for processing. Kalman filtering is known as
an effective speech enhancement technique, in which speech signal
is usually modeled as ...
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RobustadaptiveKalmanfilteringwithunknowninputs.rar
The standard optimum Kalman filter demands complete
knowledge of the system parameters, the input forcing functions, and
the noise statistics. Several adaptive methods have already been devised
to obtain the unknown information using the measurements ...
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RobustadaptiveKalmanfilteringbasedspeechenhancemen
This paper deals with the problem of speech enhancement when
only a corrupted speech signal is available for processing. Kalman
filtering is known as an effective speech enhancement technique,
in which speech signal is usually modeled as autoregressive ...
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SubspacebasedblindadaptivemultiuserdetectionusingK
... blind adaptive multiuser detection scheme based on a hybrid of Kalman filter and
subspace estimation is proposed. It is shown ... signal subspace and the coefficients can be estimated by the Kalman filter using only
the signature waveform and the timing ...
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bfl-0.4.2.rar
Klaas Gadeyne, a Ph.D. student in the Mechanical Engineering Robotics Research Group at K.U.Leuven, has developed a C++ Bayesian Filtering Library that includes software for Sequential Monte Carlo methods, Kalman filters, particle filters, etc.
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