Nonlinear Filtering and Optimal Phase Tracking

Nonfiction, Science & Nature, Science, Physics, Mathematical Physics, Mathematics, Statistics
Cover of the book Nonlinear Filtering and Optimal Phase Tracking by Zeev Schuss, Springer US
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Zeev Schuss ISBN: 9781461404873
Publisher: Springer US Publication: November 16, 2011
Imprint: Springer Language: English
Author: Zeev Schuss
ISBN: 9781461404873
Publisher: Springer US
Publication: November 16, 2011
Imprint: Springer
Language: English

 

This book offers an analytical rather than measure-theoretical approach to the derivation of the partial differential equations of nonlinear filtering theory. The basis for this approach is the discrete numerical scheme used in Monte-Carlo simulations of stochastic differential equations and Wiener's associated path integral representation of the transition probability density. Furthermore, it presents analytical methods for constructing asymptotic approximations to their solution and for synthesizing asymptotically optimal filters. It also offers a new approach to the phase tracking problem, based on optimizing the mean time to loss of lock. The book is based on lecture notes from a one-semester special topics course on stochastic processes and their applications that the author taught many times to graduate students of mathematics, applied mathematics, physics, chemistry, computer science, electrical engineering, and other disciplines. The book contains exercises and worked-out examples aimed at illustrating the methods of mathematical modeling and performance analysis of phase trackers.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

 

This book offers an analytical rather than measure-theoretical approach to the derivation of the partial differential equations of nonlinear filtering theory. The basis for this approach is the discrete numerical scheme used in Monte-Carlo simulations of stochastic differential equations and Wiener's associated path integral representation of the transition probability density. Furthermore, it presents analytical methods for constructing asymptotic approximations to their solution and for synthesizing asymptotically optimal filters. It also offers a new approach to the phase tracking problem, based on optimizing the mean time to loss of lock. The book is based on lecture notes from a one-semester special topics course on stochastic processes and their applications that the author taught many times to graduate students of mathematics, applied mathematics, physics, chemistry, computer science, electrical engineering, and other disciplines. The book contains exercises and worked-out examples aimed at illustrating the methods of mathematical modeling and performance analysis of phase trackers.

More books from Springer US

Cover of the book Intrinsic Motivation by Zeev Schuss
Cover of the book Markov Chains by Zeev Schuss
Cover of the book The Dissipation of Electromagnetic Waves in Plasmas by Zeev Schuss
Cover of the book Managing Sustainable Innovation by Zeev Schuss
Cover of the book Effect of Mineral-Organic-Microorganism Interactions on Soil and Freshwater Environments by Zeev Schuss
Cover of the book Alternative Approaches to Human Blood Resources in Clinical Practice by Zeev Schuss
Cover of the book High-Speed Clock Network Design by Zeev Schuss
Cover of the book Introduction to Clinical Skills by Zeev Schuss
Cover of the book Advances in Superprocesses and Nonlinear PDEs by Zeev Schuss
Cover of the book Smoking Prevention and Cessation by Zeev Schuss
Cover of the book Design for Manufacturing and Assembly by Zeev Schuss
Cover of the book Pathobiology of Cardiovascular Injury by Zeev Schuss
Cover of the book Ultrastructure of the Ovary by Zeev Schuss
Cover of the book Growth Hormone And The Heart by Zeev Schuss
Cover of the book Behavioral Consultation in Applied Settings by Zeev Schuss
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy