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024 7 _a10.1007/978-981-15-9426-7
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100 1 _aYan, Liping.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_932524
245 1 0 _aMultisensor Fusion Estimation Theory and Application
_h[electronic resource] /
_cby Liping Yan, Lu Jiang, Yuanqing Xia.
250 _a1st ed. 2021.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2021.
300 _aXVII, 227 p. 59 illus., 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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505 0 _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.
520 _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.
650 0 _aTelecommunication.
_910437
650 0 _aControl engineering.
_931970
650 0 _aSignal processing.
_94052
650 0 _aComputational intelligence.
_97716
650 0 _aEngineering—Data processing.
_931556
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aControl and Systems Theory.
_931972
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aData Engineering.
_932525
700 1 _aJiang, Lu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_932526
700 1 _aXia, Yuanqing.
_eauthor.
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_4http://id.loc.gov/vocabulary/relators/aut
_932527
710 2 _aSpringerLink (Online service)
_932528
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789811594281
856 4 0 _uhttps://doi.org/10.1007/978-981-15-9426-7
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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