Linear Estimation in Interconnected Sensor Systems with Information Constraints
Abstract:
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.
| Zugehörige Institution(en) am KIT | Institut für Anthropomatik und Robotik (IAR) |
| Publikationstyp | Hochschulschrift |
| Publikationsjahr | 2015 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-7315-0342-2 ISSN: 1867-3813 urn:nbn:de:0072-455771 KITopen-ID: 1000045577 |
| Verlag | KIT Scientific Publishing |
| Umfang | XVII, 227 S. |
| Serie | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory ; 16 |
| Art der Arbeit | Dissertation |
| Fakultät | Fakultät für Informatik (INFORMATIK) |
| Institut | Institut für Anthropomatik und Robotik (IAR) |
| Prüfungsdaten | 19.12.2014 |
| Schlagwörter | Datenfusion, Schätztheorie, Sensornetze, Kalman Filter, Verteilte Systems, Data fusion, estimation theory, sensor networks, Kalman filtering, distributed systems |
| Referent/Betreuer | Hanebeck, U. |