  In this manuscript, a general method for deriving filtering algorithms that
involve a network of interconnected Bayesian filters is proposed. This method
is based on the idea that the processing accomplished inside each of the
Bayesian filters and the interactions between them can be represented as
message passing algorithms over a proper graphical model. The usefulness of our
method is exemplified by developing new filtering techniques, based on the
interconnection of a particle filter and an extended Kalman filter, for
conditionally linear Gaussian systems. Numerical results for two specific
dynamic systems evidence that the devised algorithms can achieve a better
complexity-accuracy tradeoff than marginalized particle filtering and multiple
particle filtering.
