Estimation Theory with Applications to Communications and Control

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ABOUT THE BOOK:
Andrew Sage and James Melsa have succeeded in writing a comprehensive text that covers estimation theory, including decision theory, in its entirety, and examines its applications to communications and control. It is important to note at the outset that this is a book that has been written by engineers for engineers. Therefore, questions of true mathematical rigor have been largely ignored for three reasons: (1) to facilitate the delineating of basic concepts, (2) to expose the similarities and limitations among various techniques, and (3) to make this most important material accessible to the widest possible audience.
The scope of the text is broad. Beginning with basic non-time dependent linear problems, the text progresses to an examination of time dependent... Tovább

Fülszöveg

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.1, 'i,
III.
I ¦ I "l I '

' -1 I I 'I I; I ¦ I
Bl^il','. Ii-'' li 'iii!'
ABOUT THE BOOK:
Andrew Sage and James Melsa have succeeded in writing a comprehensive text that covers estimation theory, including decision theory, in its entirety, and examines its applications to communications and control. It is important to note at the outset that this is a book that has been written by engineers for engineers. Therefore, questions of true mathematical rigor have been largely ignored for three reasons: (1) to facilitate the delineating of basic concepts, (2) to expose the similarities and limitations among various techniques, and (3) to make this most important material accessible to the widest possible audience.
The scope of the text is broad. Beginning with basic non-time dependent linear problems, the text progresses to an examination of time dependent nonlinear problems. In addition, material is devoted to: stochastic processes, Gauss Markov processes and stochastic differential equations, the optimum linear filter, and extensions of the optimum linear filter. Throughout the text, modernity of approach is insured by the authors' use of the state transition viewpoint, and by extensive reference to current literature.
Also, the book Is exceedingly flexible. By rearranging certain chapters the instructor Is able to organize material suitable for several different groups of graduate students: those who have had previous experience in stochastic processes and optimization, those who have only had an Introduction to probability theory, those who desire an exposure to linear estimation only, and those who desire a complete treatment of linear and nonlinear estimation and detection. Vissza