Product Description

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.

Product Details

• Amazon Sales Rank: #1532496 in Books
• Published on: 1999-10-29
• Original language: English
• Dimensions: 1.33 pounds
• Binding: Paperback
• 393 pages

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.