Introduction to Optimal Estimation (Paperback)

Edward W. Kamen

ISBN 10: 185233133X / ISBN 13: 9781852331337

Published by Springer London Ltd, England, 1999

About this Item

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 multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781852331337

Bibliographic Details

• Title: Introduction to Optimal Estimation (Paperback)
• Publisher: Springer London Ltd, England
• Publication Date: 1999
• Language: English
• ISBN 10: 185233133X
• ISBN 13: 9781852331337
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book provides an introductory, yet comprehensive, treatment of 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. A good deal of emphasis is placed in the text on showing how these different approaches to estimation fit together to form a systematic development of optimal estimation. Included in the text is a chapter on nonlinear filtering, focusing on the extended Kalman filter (EKF) and a new measurement update that uses the Levenburg-Marquardt algorithm to obtain more accurate results in comparison to the EKF measurement update. Applications of nonlinear filtering are also considered, including the identification of nonlinear systems modeled by neural networks, FM demodulation, target tracking based on polar-coordinate measurements, and multiple target tracking.

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.