Optimal Estimation of Dynamic Systems, Second Edition (Chapman & Hall/CRC Applied Mathematics & Nonlinear Science) Review

Average Reviews:

(More customer reviews)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.

Click Here to see more reviews about: Optimal Estimation of Dynamic Systems, Second Edition (Chapman & Hall/CRC Applied Mathematics & Nonlinear Science)

Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB codes for the examples are available on the book's website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises. An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.

Get 7% OFF

Click here for more information about Optimal Estimation of Dynamic Systems, Second Edition (Chapman & Hall/CRC Applied Mathematics & Nonlinear Science)

Read More...

Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook Review

Average Reviews:

(More customer reviews)The most thorough and complete work I have seen on the subject. This provides a lot of in-depth information and insight into various areas not found elsewhere. While quite a bit of theory is presented, the main concentration is providing practical information useful for a wide variety of filter implementations. This will be most useful for somebody with a strong mathematical background, particularly in linear algebra, who is looking for a comprehensive understanding and the best solution for a particular application.

Click Here to see more reviews about: Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook

This book provides a complete explanation of estimation theory andapplication, modeling approaches, and model evaluation. Each topicstarts with a clear explanation of the theory (often includinghistorical context), followed by application issues that should beconsidered in the design. Different implementations designed to addressspecific problems are presented, and numerous examples of varyingcomplexity are used to demonstrate the concepts.This book is intended primarily as a handbook for engineers who must design practical systems. Its primarygoal is to explain all important aspects of Kalman filtering and least-squares theory and application. Discussion of estimator design and model development is emphasized so that the reader may develop an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed. Methods for deciding on the "best" model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior.A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared.The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems.The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering.

Get 24% OFF

Click here for more information about Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook

Read More...