Introduction to Optimal Estimation

Jonathan K. Su, Edward W. Kamen

ISBN 10: 185233133X / ISBN 13: 9781852331337

Published by Springer London, 1999

About this Item

Zustand: Gut | Seiten: 400 | Sprache: Englisch | Produktart: Sonstiges.

Seller Inventory # 655/3

Bibliographic Details

• Title: Introduction to Optimal Estimation
• Publisher: Springer London
• Publication Date: 1999
• Language: English
• ISBN 10: 185233133X
• ISBN 13: 9781852331337
• Binding: Soft cover
• Condition: Gut
• Seller catalogs:
  • Sonstiges

About this title

Synopsis

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 MATLAB^â. 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.

From the Back Cover

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.