LEADER 02949nam 2200481Ia 450 001 9910140851003321 005 20170816113513.0 010 $a1-283-02519-1 010 $a9786613025197 010 $a0-470-89004-5 010 $a0-470-89003-7 035 $a(CKB)2670000000066225 035 $a(EBL)644860 035 $a(OCoLC)705350791 035 $a(MiAaPQ)EBC644860 035 $a(EXLCZ)992670000000066225 100 $a20080229d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 200 10$aAdvanced Kalman filtering, least-squares and modeling$b[electronic resource] $ea practical handbook /$fBruce P. Gibbs 210 $aHoboken, NJ $cWiley Pub.$dc2011 215 $a1 online resource (627 p.) 300 $aDescription based upon print version of record. 311 $a0-470-52970-9 320 $aIncludes bibliographical references and index. 327 $aADVANCED 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 327 $aCHAPTER 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 330 $aThis 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 606 $aKalman filtering 606 $aLeast squares 608 $aElectronic books. 615 0$aKalman filtering. 615 0$aLeast squares. 676 $a620.0042 676 $a620.0072/7 700 $aGibbs$b Bruce. P.$f1946-$0854293 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140851003321 996 $aAdvanced Kalman filtering, least-squares and modeling$91907728 997 $aUNINA