  In this paper, low-complexity distributed fusion filtering algorithm for
mixed continuous-discrete multisensory dynamic systems is proposed. To
implement the algorithm a new recursive equations for local cross-covariances
are derived. To achieve an effective fusion filtering the covariance
intersection (CI) algorithm is used. The CI algorithm is useful due to its
low-computational complexity for calculation of a big number of
cross-covariances between local estimates and matrix weights. Theoretical and
numerical examples demonstrate the effectiveness of the covariance intersection
algorithm in distributed fusion filtering.
