LEADER 04602nam 22007335 450 001 9910254586603321 005 20200706024016.0 010 $a3-319-47612-2 024 7 $a10.1007/978-3-319-47612-4 035 $a(CKB)3710000001127544 035 $a(DE-He213)978-3-319-47612-4 035 $a(MiAaPQ)EBC6314537 035 $a(MiAaPQ)EBC5588922 035 $a(Au-PeEL)EBL5588922 035 $a(OCoLC)981460736 035 $a(PPN)19976932X 035 $a(EXLCZ)993710000001127544 100 $a20170322d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKalman Filtering $ewith Real-Time Applications /$fby Charles K. Chui, Guanrong Chen 205 $a5th ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVIII, 247 p. 34 illus.) 311 $a3-319-47610-6 327 $aPreliminaries -- Kalman Filter: An Elementary Approach -- Orthogonal Projection and Kalman Filter -- Correlated System and Measurement Noise Processes -- Colored Noise -- Limiting Kalman Filter -- Sequential and Square-Root Algorithms -- Extended Kalman Filter and System Identification -- Decoupling of Filtering Equations -- Kalman Filtering for Interval Systems -- Wavelet Kalman Filtering -- Distributed Estimation on Sensor Networks -- Notes -- Answers and Hints to Exercises. 330 $aThis new edition 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. 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. Over 100 exercises and problems with solutions help deepen the knowledge. This new edition has a new chapter on filtering communication networks and data processing, together with new exercises and new real-time applications. 606 $aPhysics 606 $aEconomic theory 606 $aApplied mathematics 606 $aEngineering mathematics 606 $aElectrical engineering 606 $aArtificial intelligence 606 $aMathematical Methods in Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P19013 606 $aNumerical and Computational Physics, Simulation$3https://scigraph.springernature.com/ontologies/product-market-codes/P19021 606 $aEconomic Theory/Quantitative Economics/Mathematical Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/W29000 606 $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aPhysics. 615 0$aEconomic theory. 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 0$aElectrical engineering. 615 0$aArtificial intelligence. 615 14$aMathematical Methods in Physics. 615 24$aNumerical and Computational Physics, Simulation. 615 24$aEconomic Theory/Quantitative Economics/Mathematical Methods. 615 24$aMathematical and Computational Engineering. 615 24$aCommunications Engineering, Networks. 615 24$aArtificial Intelligence. 676 $a515 700 $aChui$b Charles 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 $a9910254586603321 996 $aKalman Filtering$92182298 997 $aUNINA