LEADER 04310nam 22008055 450 001 9910968876903321 005 20250730101928.0 010 $a3-662-03859-5 024 7 $a10.1007/978-3-662-03859-8 035 $a(CKB)2660000000026262 035 $a(SSID)ssj0000855046 035 $a(PQKBManifestationID)11443509 035 $a(PQKBTitleCode)TC0000855046 035 $a(PQKBWorkID)10912204 035 $a(PQKB)10719062 035 $a(DE-He213)978-3-662-03859-8 035 $a(MiAaPQ)EBC3098451 035 $a(PPN)237922746 035 $a(EXLCZ)992660000000026262 100 $a20130130d1999 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aKalman Filtering $ewith Real-Time Applications /$fby Charles K. Chui, Guanrong Chen 205 $a3rd ed. 1999. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1999. 215 $a1 online resource (XIV, 230 p.) 225 1 $aSpringer Series in Information Sciences ;$v17 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-64611-6 320 $aIncludes bibliographical references and index. 327 $a1. Preliminaries -- 2. Kalman Filter: An Elementary Approach -- 3. Orthogonal Projection and Kalman Filter -- 4. Correlated System and Measurement Noise Processes -- 5. Colored Noise -- 6. Limiting Kalman Filter -- 7. Sequential and Square-Root Algorithms -- 8. Extended Kalman Filter and System Identification -- 9. Decoupling of Filtering Equations -- 10. Kalman Filtering for Interval Systems -- 11. Wavelet Kalman Filtering -- 12. Notes -- References -- Answers and Hints to Exercises. 330 $aKalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. The last two topics are new additions to this third edition. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. 410 0$aSpringer Series in Information Sciences ;$v17 606 $aMathematical physics 606 $aEconometrics 606 $aEngineering mathematics 606 $aEngineering$xData processing 606 $aTelecommunication 606 $aArtificial intelligence 606 $aMathematical Methods in Physics 606 $aTheoretical, Mathematical and Computational Physics 606 $aQuantitative Economics 606 $aMathematical and Computational Engineering Applications 606 $aCommunications Engineering, Networks 606 $aArtificial Intelligence 615 0$aMathematical physics. 615 0$aEconometrics. 615 0$aEngineering mathematics. 615 0$aEngineering$xData processing. 615 0$aTelecommunication. 615 0$aArtificial intelligence. 615 14$aMathematical Methods in Physics. 615 24$aTheoretical, Mathematical and Computational Physics. 615 24$aQuantitative Economics. 615 24$aMathematical and Computational Engineering Applications. 615 24$aCommunications Engineering, Networks. 615 24$aArtificial Intelligence. 676 $a629.8/312 700 $aChui$b C. K.$4aut$4http://id.loc.gov/vocabulary/relators/aut$022718 702 $aChen$b Guanrong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910968876903321 996 $aKalman Filtering$92182298 997 $aUNINA