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210512 ||| eng |
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|a 9783731503422
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|a 1000045577
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|a Reinhardt, Marc
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| 245 |
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|a Linear Estimation in Interconnected Sensor Systems with Information Constraints
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2015
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| 300 |
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|a 1 electronic resource (XVII, 227 p. p.)
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| 653 |
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|a sensor networks
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| 653 |
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|a distributed systems
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| 653 |
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|a Verteilte SystemsData fusion
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| 653 |
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|a Schätztheorie
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| 653 |
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|a Sensornetze
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| 653 |
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|a Kalman Filter
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| 653 |
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|a Datenfusion
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| 653 |
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|a Kalman filtering
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| 653 |
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|a estimation theory
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
0 |
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|a Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-sa/4.0/
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| 024 |
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|a 10.5445/KSP/1000045577
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| 856 |
4 |
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|u https://www.ksp.kit.edu/9783731503422
|7 0
|x Verlag
|3 Volltext
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/51747
|z DOAB: description of the publication
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| 520 |
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|a A 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.
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