| 000 | 01796naaaa2200349uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/51747 | ||
| 005 | 20220220082048.0 | ||
| 020 | _aKSP/1000045577 | ||
| 020 | _a9783731503422 | ||
| 024 | 7 |
_a10.5445/KSP/1000045577 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 100 | 1 |
_aReinhardt, Marc _4auth |
|
| 245 | 1 | 0 | _aLinear Estimation in Interconnected Sensor Systems with Information Constraints |
| 260 |
_bKIT Scientific Publishing _c2015 |
||
| 300 | _a1 electronic resource (XVII, 227 p. p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _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. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by-sa/4.0/ _2cc _4https://creativecommons.org/licenses/by-sa/4.0/ |
||
| 546 | _aEnglish | ||
| 653 | _aSchätztheorie | ||
| 653 | _aKalman Filter | ||
| 653 | _aestimation theory | ||
| 653 | _aSensornetze | ||
| 653 | _aVerteilte SystemsData fusion | ||
| 653 | _adistributed systems | ||
| 653 | _aDatenfusion | ||
| 653 | _asensor networks | ||
| 653 | _aKalman filtering | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://www.ksp.kit.edu/9783731503422 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/51747 _70 _zDOAB: description of the publication |
| 999 |
_c75642 _d75642 |
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