Product Description

Introduction to Optimal Estimation is an introductory but comprehensive treatment of the important topics of Kalman and Wiener filtering. In addition, least-squares, maximum-likelihood and maximum a posteriori (based on discrete-time measurements) estimation are developed, covering a broad range of techniques in a single textbook. Emphasis is placed on showing how these different approaches can be fitted together to form a systematic rationale for optimal estimation. The different matters to be addressed in actually computing estimates and characterizing the properties of estimates viewed as random variables are explained and underlined throughout. The text also incorporates study of nonlinear filtering, focusing on the extended Kalman filter and on a recently-developed nonlinear estimator based on a block-form version of the Levenberg-Marquardt algorithm.

Introduction to Optimal Estimation is for use in a single course (or, with judicious pruning, a one-quarter course) on estimation by senior undergraduates or first-year graduate students. A number of the examples in this text were fashioned using MATLAB^® and some of the homework problems require it. Students using this book will need to have completed a standard course on probability and random variables and at least one course in signals and systems including state-space theory for linear systems.

Product Details

• Amazon Sales Rank: #701555 in Books
• Published on: 1999-10-29
• Original language: English
• Binding: Paperback
• 380 pages