Introduction to Optimal Estimation

By Edward W. Kamen and Jonathan K. Su

Book Overview

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(R) 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 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.

• Format: Paperback
• Language: English
• ISBN: 185233133X
• ISBN13: 9781852331337
• Release Date: September 1999
• Publisher: Springer
• Length: 380 Pages
• Weight: 1.30 lbs.
• Dimensions: 1.0" x 6.1" x 9.2"