Introduction to Optimal Estimation (Paperback)

Edward W. Kamen

ISBN 10: 185233133X / ISBN 13: 9781852331337

Verlag: Springer London Ltd, England, 1999

Beschreibung

Paperback. Developed from a set of lecture notes by Professor Kamen and since developed and refined by both authors, this introductory yet comprehensive study is a prime example in its field. There are examples in the book that use MATLAB and many of the problems discussed require the use of MATLABa. The primary objective is to provide students with an extensive coverage of Wiener and Kalman filtering along with the development of least squares estimation, maximum likelihood estimation and maximum a posteriori estimation, based on discrete-time measurements. In the study of these estimation techniques there is a strong emphasis on how they interrelate and fit together to form a systematic development of optimal estimation. Also included in the text is a chapter on nonlinear filtering focusing on the extended Kalman filter and a recently-developed nonlinear estimator based on a block-form version of the Levenberg-Marquardt algorithm. An introduction to both Wiener and Kalman filtering along with a development of least-squares estimation, maximum likelihood estimation, and maximum a posteriori estimation based on discrete-time measurements. MATLAB is used in some of the examples and required for many of the homework problems. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Bestandsnummer des Verkäufers 9781852331337

Bibliografische Details

• Titel: Introduction to Optimal Estimation (Paperback)
• Verlag: Springer London Ltd, England
• Erscheinungsjahr: 1999
• Sprache: Englisch
• ISBN-10: 185233133X
• ISBN-13: 9781852331337
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

A handy technical introduction to the latest theories and techniques of optimal estimation. It provides readers with extensive coverage of Wiener and Kalman filtering along with a development of least squares estimation, maximum likelihood and maximum a posteriori estimation based on discrete-time measurements. Much emphasis is placed on how they interrelate and fit together to form a systematic development of optimal estimation. Examples and exercises refer to MATLAB software.

Von der hinteren Coverseite

This book, developed from a set of lecture notes by Professor Kamen, and since expanded and refined by both authors, is an introductory yet comprehensive study of its field. It contains examples that use MATLAB^® and many of the problems discussed require the use of MATLAB^®. The primary objective is to provide students with an extensive coverage of Wiener and Kalman filtering along with the development of least squares estimation, maximum likelihood estimation and a posteriori estimation, based on discrete-time measurements. In the study of these estimation techniques there is strong emphasis on how they interrelate and fit together to form a systematic development of optimal estimation. Also included in the text is a chapter on nonlinear filtering, focusing on the extended Kalman filter and a recently-developed nonlinear estimator based on a block-form version of the Levenberg-Marquadt Algorithm.