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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Information form distributed Kalman filtering (IDKF) with explicit inputs
 Institute of Electrical and Electronics Engineers IEEE; IEEE Aerospace and Electronic Systems Society AESS: 20th International Conference on Information Fusion 2017. Proceedings : Xi'an, China, 1013 July 2017 Piscataway, NJ: IEEE, 2017 ISBN: 9780996452700 ISBN: 9781509045822 S.743750 
 International Conference on Information Fusion (FUSION) <20, 2017, Xi'an> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer FKIE () 
Abstract
With the ubiquity of information distributed in networks, performing recursive Bayesian estimation using distributed calculations is becoming more and more important. There are a wide variety of algorithms catering to different applications and requiring different degrees of knowledge about the other nodes involved. One recently developed algorithm is the distributed Kalman filter (DKF), which assumes that all knowledge about the measurements, except the measurements themselves, are known to all nodes. If this condition is met, the DKF allows deriving the optimal estimate if all information is combined in one node at an arbitrary time step. In this paper, we present an information form of the distributed Kalman filter (IDKF) that allows the use of explicit system inputs at the individual nodes while still yielding the same results as a centralized Kalman filter.