LEADER 01904nam  2200421z- 450 
001 9910346783203321
005 20210211
010   $a1000045577
035   $a(CKB)4920000000100691
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/51747
035   $a(oapen)doab51747
035   $a(EXLCZ)994920000000100691
100   $a20202102d2015      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aLinear Estimation in Interconnected Sensor Systems with Information Constraints
210   $cKIT Scientific Publishing$d2015
215   $a1 online resource (XVII, 227 p. p.)
225 1 $aKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
311 08$a3-7315-0342-5 
330   $aA ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.
610   $aDatenfusion
610   $adistributed systems
610   $aestimation theory
610   $aKalman Filter
610   $aKalman filtering
610   $aScha?tztheorie
610   $asensor networks
610   $aSensornetze
610   $aVerteilte SystemsData fusion
700   $aReinhardt$b Marc$4auth$01318463
906   $aBOOK
912   $a9910346783203321
996   $aLinear Estimation in Interconnected Sensor Systems with Information Constraints$93033287
997   $aUNINA