Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale
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Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation.

Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals received are used to determine highly sensitive processes such as the flight path of a plane, the orbit of a space vehicle, or the control of a machine. The authors use dynamic models from mechanical and aerospace engineering to provide immediate results of estimation concepts with a minimal reliance on mathematical skills. The book documents the development of the central concepts and methods of optimal estimation theory in a manner accessible to engineering students, applied mathematicians, and practicing engineers. It includes rigorous theoretial derivations and a significant amount of qualitiative discussion and judgements. It also presents prototype algorithms, giving detail and discussion to stimulate development of efficient computer programs and intelligent use of them.

This book illustrates the application of optimal estimation methods to problems with varying degrees of analytical and numercial difficulty. It compares various approaches to help develop a feel for the absolute and relative utility of different methods, and provides many applications in the fields of aerospace, mechanical, and electrical engineering.

Review

A nice feature of this book is that it makes the effort to explain the underlying principles behind the formula for each algorithm; the relationship between different algorithms is equally well addressed. … The text is a good combination of theory and practice. It will be a valuable addition to references for academic researchers and industrial engineers working in the field of estimation. It will also serve as a useful reference for graduate courses in control and estimation.
- AIAA Journal, Vol. 43, No. 1, January 2005

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4.1 out of 54.1 out of 5
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5.0 out of 5 starsVerified Purchase
Good balance between theory and practice
Reviewed in the United States on December 18, 2014
My review will cover most chapters except the applications, since I have not read them at all. The book is indeed rigorous; it provides mathematical derivations for most of the main algorithms, and it does so in a nice way. But it is not strictly a theoretical... See more
My review will cover most chapters except the applications, since I have not read them at all.

The book is indeed rigorous; it provides mathematical derivations for most of the main algorithms, and it does so in a nice way. But it is not strictly a theoretical book; it has lots of nice practical examples that provide a bridge between theory and practice, something that I like and did not find in other books.

The discussion about Kalman Filter and Extended Kalman Filter is excellent. That for the Unscented Kalman Filter is not as excellent, but still good. I liked the section at the end of the book that describe the Linear Quadratic Controller, and how it puts the optimal filter (Kalman Filter) in a "bigger picture". I also liked the appendix that reviews the dynamic systems.

In brief, if you are an engineer wishing to use Kalman Filter (and others) but want to know how did the equations came instead of blindly applying them in Matlab, then this book is for you.
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Nicholas Huff
5.0 out of 5 starsVerified Purchase
Excellent Chapters on Kalman Filtering
Reviewed in the United States on September 11, 2011
I particularly enjoyed this book''s introduction to the Kalman filter. Nothing I had ever read before could really give me a good "feel" for how the Kalman filter works and what it is actually doing when it is forming an estimate. The book''s approach for introducing the KF... See more
I particularly enjoyed this book''s introduction to the Kalman filter. Nothing I had ever read before could really give me a good "feel" for how the Kalman filter works and what it is actually doing when it is forming an estimate. The book''s approach for introducing the KF is to first give a review of least mean squares estimation. Every engineering student (and a lot of students of other subjects) has used least mean squares. It is just basic curve fitting. It then goes on to describe weighted least mean squares curve fitting, which is just least mean squares with "weights" assigned to individual measurements based on their uncertainties. After that, the authors derive an algorithm for performing the curve fit as the measurements come in one at a time (sequentially), rather than using all the measurements at once as you do in weighted least mean squares. Well, that algorithm is the basic linear Kalman Filter. Approached from the direction that the authors use, it was very easy to understand. They also give very good introductions to the extended Kalman filter, and even the "unscented" Kalman filter (webpage tutorials on the UKF absolutely suck, the book has a much better dicussion on this topic). This book is now my primary reference text on Kalman fitering.
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Kim,Jong-Woo
5.0 out of 5 starsVerified Purchase
Outstanding inclusive text on estimation theory!
Reviewed in the United States on June 3, 2004
It presents the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for navigation and tracking, vehicle attitude determination. There is a lot of material that is covered by this book. The examples are well presented and they... See more
It presents the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for navigation and tracking, vehicle attitude determination. There is a lot of material that is covered by this book. The examples are well presented and they really help you when working on the problems at the end of each chapter. Also, computer routines for all the examples shown in the text can be accessed. I have to say that this is an excellent book for estimation of dynamic systems.
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David Cowdin
1.0 out of 5 starsVerified Purchase
I am unhappy with it because there is a 2nd ...
Reviewed in the United States on January 27, 2018
I am unhappy with it because there is a 2nd edition out there and it wasn''t offered on Amazon. I didn''t realize it until I went to one of the authors website and saw that there was addition published 12 years later!! I am sure this was a substantial update after that... See more
I am unhappy with it because there is a 2nd edition out there and it wasn''t offered on Amazon. I didn''t realize it until I went to one of the authors website and saw that there was addition published 12 years later!! I am sure this was a substantial update after that amount of time. This is a common thing with Amazon. They offer the "dregs" edition for a low price, knowing that there is a newer version and you only find it after you go to the authors website and find out. The second edition might be worth it.
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Michael Turmon
5.0 out of 5 stars
Well balanced and inclusive
Reviewed in the United States on August 10, 2010
This book covers a lot of ground. The authors are careful to distinguish between all types of state/measurement systems: those where the state evolution is continuous VS. discrete, and those where the measurement times are continuous VS. discrete. Also, all the "batch... See more
This book covers a lot of ground. The authors are careful to distinguish between all types of state/measurement systems: those where the state evolution is continuous VS. discrete, and those where the measurement times are continuous VS. discrete. Also, all the "batch estimation" cases are covered (the various types of smoothers). The treatment of the EKF is similarly careful, covering several ways the EKF can be implemented. The update equations for each case are pulled out as a table for reference.

The key was, they''re not just trying to explain some main cases so you get the idea (like a first textbook should). They''re trying to comprehensively address ALL cases, so that your situation is in there.

The authors also do a good job with examples, and conveying intuition (way beyond just "the error is orthogonal to the innovation"). Most of the examples compare one implementation with another, so you can develop understanding of the limits and benefits of techniques.

The somewhat drab cover of this book made it seem like it was going to be poorly typeset and edited. It''s fine on the inside.
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Brian Vandenberg
5.0 out of 5 stars
Very readable, well written; requires strong math skills
Reviewed in the United States on October 22, 2010
I''m a computer science & applied math graduate (undergrad) working as a computer scientist. I''m studying these and other topics in my spare time, not for grad school (at least, not yet). I love this book, and I''m thoroughly impressed with how well it is... See more
I''m a computer science & applied math graduate (undergrad) working as a computer scientist. I''m studying these and other topics in my spare time, not for grad school (at least, not yet).

I love this book, and I''m thoroughly impressed with how well it is written. It is the first book I''ve read with more than a brief treatment of calculus concepts in tandem with linear algebra (eg, derivatives with respect to a vector or matrix, differential equations involving matrix expressions, etc).

When reading some other books (eg, Haykin''s Adaptive Filter Theory), I find myself staring blankly at pages, my thoughts drifting to other unrelated topics, and ultimately I have to re-read sections many times before a point sinks in -- even then, I feel as though some points are still eluding me.

In stark contrast to Haykin''s book, Crassidis & Junkins do an excellent job of presenting concepts and briefly sketching proofs where necessary, while keeping the material interesting and approachable.

I''m tempted to drop a star because there are times while reading where one or more steps are left out, or a minor mistake leaves the observant reader to scratch their head for awhile before it becomes clear that a mistake was made -- eg, a T (for transpose) was left out of an expression -- but mistakes happen; just get the errata from the author''s website, and hope you don''t get stuck anywhere else not covered by the errata.

-Brian
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Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale

Optimal popular Estimation of Dynamic Systems (Chapman & Hall/CRC Applied Mathematics outlet sale & Nonlinear Science) outlet sale