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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|---|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvanced Kalman filtering, least-squares and modeling $ea practical handbook /$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   $aLeast squares
615  0$aKalman filtering.
615  0$aLeast squares.
676   $a620.0042
676   $a620.0072/7
700   $aGibbs$b Bruce. P.$f1946-$01762618
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910877349703321
996   $aAdvanced Kalman filtering, least-squares and modeling$94202649
997   $aUNINA