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Discrete Stochastic Processes and Optimal Filtering

This title is concerned with the founding principles of optimal filters. It proposes several reminders about both random vectors and Gaussian vectors. The study of discrete time processes makes it possible to tackle digital filtering; a chapter on estimation gives the principle results necessary for the construction of the Wiener filter and of the adaptive filter used in the case of stationary signals. It concludes with an examination of Kalman filtering which extends optimal filtering to the case of non-stationary signals. Exercises with solutions punctuate each chapter and practical examples.

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  • "This title is concerned with the founding principles of optimal filters. It proposes several reminders about both random vectors and Gaussian vectors. The study of discrete time processes makes it possible to tackle digital filtering; a chapter on estimation gives the principle results necessary for the construction of the Wiener filter and of the adaptive filter used in the case of stationary signals. It concludes with an examination of Kalman filtering which extends optimal filtering to the case of non-stationary signals. Exercises with solutions punctuate each chapter and practical examples."@en
  • "Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which are used in relation to non-stationary signals. Exercises with solutions feature in each chapter to demonstrate the practical application of these ideas using MATLAB"
  • "Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which ar."@en
  • "This title is concerned with the founding principles of optimal filters. It proposes several reminders about both random vectors and Gaussian vectors. The study of discrete time processes makes it possible to tackle digital filtering; a chapter on estimation gives the principle results necessary for the construction of the Wiener filter and of the adaptive filter used in the case of stationary signals. It concludes with an examination of Kalman filtering which extends optimal filtering to the case of non-stationary signals. Exercises with solutions punctuate each chapter and practical examples are dealt with using Matlab software. This work is aimed at graduate students and engineers as well as members of the scientific community who wish to rediscover the founding principles of optimal filters."@en
  • "Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which are used in relation to non-stationary signals. Exercises with solutions feature in each chapter to demonstrate the practical application of these ideas using MATLAB."

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  • "Livres électroniques"
  • "Electronic books"
  • "Electronic books"@en
  • "Ressources internet"

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  • "Discrete Stochastic Processes and Optimal Filtering"
  • "Discrete Stochastic Processes and Optimal Filtering"@en
  • "Processus stochastiques discrets et filtrages optimaux"
  • "Discrete stochastic processes and optimal filtering"@en
  • "Discrete stochastic processes and optimal filtering"

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