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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.

Schätztheorie --- Kalman Filter --- estimation theory --- Sensornetze --- Verteilte SystemsData fusion --- distributed systems --- Datenfusion --- sensor networks --- Kalman filtering