1.

Record Nr.

UNINA9910140851003321

Autore

Gibbs Bruce. P. <1946->

Titolo

Advanced Kalman filtering, least-squares and modeling [[electronic resource] ] : a practical handbook / / Bruce P. Gibbs

Pubbl/distr/stampa

Hoboken, NJ, : Wiley Pub., c2011

ISBN

1-283-02519-1

9786613025197

0-470-89004-5

0-470-89003-7

Descrizione fisica

1 online resource (627 p.)

Disciplina

620.0042

620.0072/7

Soggetti

Kalman filtering

Least squares

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

ADVANCED KALMAN FILTERING, LEAST-SQUARES AND MODELING; CONTENTS; PREFACE; CHAPTER 1: INTRODUCTION; CHAPTER 2: SYSTEM DYNAMICS AND MODELS; CHAPTER 3: MODELING EXAMPLES; CHAPTER 4: LINEAR LEAST - SQUARES ESTIMATION: FUNDAMENTALS; CHAPTER 5: LINEAR LEAST - SQUARES ESTIMATION: SOLUTION TECHNIQUES; CHAPTER 6: LEAST - SQUARES ESTIMATION: MODEL ERRORS AND MODEL ORDER; 6.1 ASSESSING THE VALIDITY OF THE SOLUTION; 6.2 SOLUTION ERROR ANALYSIS; 6.3 REGRESSION ANALYSIS FOR WEIGHTED LEAST SQUARES; 6.4 SUMMARY; CHAPTER 7: LEAST - SQUARES ESTIMATION: CONSTRAINTS, NONLINEAR MODELS, AND ROBUST TECHNIQUES

CHAPTER 8: KALMAN FILTERINGCHAPTER 9: FILTERING FOR NONLINEAR SYSTEMS, SMOOTHING, ERROR ANALYSIS/MODEL DESIGN, AND MEASUREMENT PREPROCESSING; CHAPTER 10: FACTORED (SQUARE - ROOT) FILTERING; CHAPTER 11: ADVANCED FILTERING TOPICS; CHAPTER 12: EMPIRICAL MODELING; APPENDIX A: SUMMARY OF VECTOR/MATRIX OPERATIONS; APPENDIX B: PROBABILITY AND RANDOM



VARIABLES; BIBLIOGRAPHY; INDEX

Sommario/riassunto

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 estim