Distributed Kalman filter fusion at arbitrary instants of time

Track-to-track fusion aims at combining locally preprocessed information of individual sensors optimally, i.e. in a way that is equivalent to fusing all measurements of all sensors directly. It is well known that this can be achieved if the local sensor tracks produced at all individual scan times are available in the fusion center. Full-rate communication in this sense, however, is impractical in certain applications. In [1] W. Koch thus proposes a distributed Kalman-type processing scheme, which provides optimal track-to-track fusion results at arbitrarily chosen instants of time by communicating and combining the local sensor `tracks´ referring to this time. However, this scheme needs an exchange of sensor data for the track prediction and retrodiction, if the number of sensors exceeds two. Therefore, we present an improvement, which extends the algorithm to arbitrary sensor count.