"The dozens of helpful step-by-step examples, visual illustrations, and lists of exercises proposed at the end of each chapter significantly facilitate a reader's understanding of the book's content." (Computing Reviews.com, December 4, 2006)
This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering.
While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning:
Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering.
Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. A solutions manual is available for instructors.
With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
I strongly recommend this book to everyone who is interested in Kalman filter theory.
A very clear, well written book that takes you step by step from the algebra and statistics basics to the most advanced developments of dynamic systems.
Dan uses well laid out algorithmic approaches, suitable for programming, and examples to explain the details and show the complexities in action.
when you get to the more advanced topics in engineering, most material are written by people who spend their whole lives researching small topics. Read morePublished 3 months ago by H. Rastgar
I've got a fairly extensive background in applications of estimation, but I needed to learn about some of the extensions of the theory, so I bought this primarily for Chapters 7... Read morePublished 6 months ago by K. Hicks
This book seems to strike a good balance among the points I've been looking for in a state estimation book- coverage, explanation, derivation, and application. Read morePublished 7 months ago by Control_Engr
I am a researcher and my background is in estimation, prediction modeling, and inferential models/methods. Read morePublished 17 months ago by Waseem
The book is so nice,it explains the kalman filter very well and simple!also it covers the new topics related to kalman filtering.Published 19 months ago by Ali
I'm already familiar with the topic of Kalman filtering and I bought this book as a reference, but I find that it works poorly for that. Read morePublished 19 months ago by Murray R Dunn
The book is enjoyable as an easy to read and understand primer to filtering. Written in a fluid and eloquent style. Read morePublished 20 months ago by Bilal A. Siddiqui
This is an excellent book to learn estimation theory. I found the introductory chapter very helpful. The chapters on EKF and UKF are quite good. Highly recommended.Published on December 3, 2011 by Srikumar Sandeep
Anyone who has a formal education on estimation theory will find this book useless.
First of all, the author seems to lack a solid understanding of any kind of... Read more