Produktbild: Fundamentals of Stochastic Signals, Systems and Estimation Theory

Fundamentals of Stochastic Signals, Systems and Estimation Theory With worked Examples

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.09.2008

Verlag

Springer Berlin

Seitenzahl

380

Maße (L/B/H)

24.8/17.5/2.9 cm

Gewicht

830 g

Auflage

2nd ed.

Sprache

Englisch

ISBN

978-3-540-70990-9

Beschreibung

Rezension

From the reviews of the second edition:

"In the present textbook basic concepts of linear stochastic systems, stochastic signals, modeling and analysis, as well as model-based signal processing are described using the transfer function model and the state space model. ... Many worked examples are given within the text. The book is suitable for undergraduate and graduate courses in the field of linear stochastic systems, signal processing and automatic control." (Kurt Marti, Zentralblatt MATH, Vol. 1156, 2009)

"The book presents the fundamentals of stochastic processes and systems, with emphasis on estimation theory. It is appropriate for both undergraduate and graduate students and for engineers in the fields of communications, signal processing, and automatic control. It contains several examples and experiments performed by using Matlab. ... Appendices on Laplace and Z-transforms, matrix analysis, and probability density functions and references end the book." (Antonio Napolitano, Mathematical Reviews, Issue 2010 j)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.09.2008

Verlag

Springer Berlin

Seitenzahl

380

Maße (L/B/H)

24.8/17.5/2.9 cm

Gewicht

830 g

Auflage

2nd ed.

Sprache

Englisch

ISBN

978-3-540-70990-9

Herstelleradresse

Springer Heidelberg
Tiergartenstr. 17
69121 Heidelberg
DE
buchhandel-buch@springer.com

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  • Produktbild: Fundamentals of Stochastic Signals, Systems and Estimation Theory
  • Review of The Theory of Probability and Random Variables.- Fundamentals of Stochastic Processes.- Linear Discrete-Time Stochastic Systems.- Linear Continuous Time Stochastic Systems.- Fundamentals of Estimation.- Optimum Nonrecursive Linear Estimation: Wiener Filtering.- Optimum Recursive Linear Estimation: Kalman Filtering.- Extensions of The Optimum Recursive (Kalman) Filter.