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Kalman Filter Recent Advances and Applications
The discussion about the manned spacecraft program was initiated at NASA in 1959.
Only one year later, Dr. Kalman and Dr. Schmidt linked the linear Kalman filter and the
perturbation theory in order to obtain the Kalman-Schmidt filter, currently known as the
extended Kalman filter. This approach would be implemented in 1961 using an... | | Subspace Methods for System IdentificationSystem identification provides methods for the sensible approximation of real systems using a model set based on experimental input and output data. Tohru Katayama sets out an in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results. The text is... | | Web Security Field GuideWhile the Internet has transformed and improved the way we do business, this vast network and its associated technologies have opened the door to an increasing number of security threats. The challenge for successful, public web sites is to encourage access to the site while eliminating undesirable or malicious traffic and to provide sufficient... |
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| | Modern Control Design With MATLAB and SIMULINK
The motivation for writing this book can be ascribed chiefly to the usual struggle of
an average reader to understand and utilize controls concepts, without getting lost in
the mathematics. Many textbooks are available on modern control, which do a fine
job of presenting the control theory. ... | | Approximate Kalman Filtering (Approximations and Decompositions)
Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modelling; ideal will-conditioned matrices in computation and strictly... |
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| | Haskell Financial Data Modeling and Predictive Analytics
Haskell is one of the three most influential functional programming languages available today along with Lisp and Standard ML. When used for financial analysis, you can achieve a much-improved level of prediction and clear problem descriptions.
Haskell Financial Data Modeling and Predictive Analytics is a hands-on guide that... | | Modeling, Estimation and Optimal Filtration in Signal Processing
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.
Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are... |
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