This book is intended primarily as a handbook for engineers who must design practical systems.
Its primary goal is to discuss model development in sufficient detail so that the reader may design 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.
Supplemental materials and up-to-date errata are downloadable at http://booksupport.wiley.com.
The only book to cover least-squares estimation, Kalman filtering, and model development This book provides a complete explanation of estimation theory and application, modeling approaches, and model evaluation. Each topic starts with a clear explanation of the theory (often including historical context), followed by application issues that should be considered in the design. Different implementations designed to address specific problems are presented, and numerous examples of varying complexity are used to demonstrate the concepts.
It focuses on practical methods for developing and implementing least-squares estimators, Kalman filters, and newer filtering techniques. Since model development is critical to a successful implementation, the book discusses first-principle approaches, basis function expansions, stochastic models, and ARMA-type structures. Computation of empirical models and determination of "best" model structures and order are also discussed. The text is written to help the reader design an estimator that meets all application requirements.
Specifically addressed are methods for developing models that meet estimation goals, procedures for making the estimator robust to modeling and numerical errors, extensions of the basic methods for handling non-ideal systems, and techniques for evaluating performance and analyzing accuracy problems. Including many real-world examples, the book:
- Presents little-known extensions of least-squares estimation and Kalman filtering that provide guidance on model structure and parameters
- Explains numerical accuracy, computational burden, and modeling tradeoffs for real-world applications
- Discusses implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared
- Offers guidance in evaluating estimator performance and in determining/correcting problems
- A related Web site provides a subroutine library that simplifies implementation, as well as general purpose high-level drivers that allow for the easy analysis of alternative models and access to extensions of the basic Kalman filtering
Drawing from four decades of the author's experience with the material, Advanced Kalman Filtering, Least-Squares and Modeling is a comprehensive and detailed explanation of these topics. Practicing engineers, designers, analysts, and students using estimation theory to develop practical systems will find this a very useful reference.
