LEADER 03415oam 2200505 450 001 9910484365303321 005 20210420163223.0 010 $a981-15-9426-0 024 7 $a10.1007/978-981-15-9426-7 035 $a(CKB)4100000011569116 035 $a(DE-He213)978-981-15-9426-7 035 $a(MiAaPQ)EBC6389924 035 $a(PPN)252505476 035 $a(EXLCZ)994100000011569116 100 $a20210420d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultisensor fusion estimation theory and application /$fLiping Yan, Lu Jiang and Yuanqing Xia 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (XVII, 227 p. 59 illus., 46 illus. in color.) 311 $a981-15-9425-2 327 $aIntroduction to Optimal Fusion Estimation and Kalman Filtering: Preliminaries -- Kalman Filtering of Discrete Dynamic Systems -- Optimal Kalman filtering Fusion for Linear Dynamic Systems with Cross-Correlated Sensor Noises -- Distributed Data Fusion for Multirate Sensor Networks -- Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise -- Fusion Estimation for Asynchronous Multirate Multisensor Systems with Unreliable Measurements and Coupled Noises -- Multi-sensor Distributed Fusion Estimation for Systems with Network Delays, Uncertainties and Correlated Noises -- Event-triggered Centralized Fusion Estimation for Dynamic Systems with Correlated Noises -- Event-triggered Distributed Fusion Estimation for WSN Systems -- Event-triggered Sequential Fusion Estimation for Dynamic Systems with Correlated Noises -- Distributed Fusion Estimation for Multisensor Systems with Heavy-tailed Noises -- Sequential Fusion Estimation for Multisensor Systems with Heavy-tailed Noises. 330 $aThis book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc. 606 $aSignal processing 606 $aAutomatic control 606 $aElectrical engineering 615 0$aSignal processing. 615 0$aAutomatic control. 615 0$aElectrical engineering. 676 $a621.3 700 $aYan$b Liping$01226007 702 $aXia$b Yuanqing 702 $aJiang$b Lu 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910484365303321 996 $aMultisensor fusion estimation theory and application$92846500 997 $aUNINA