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.
