  In this manuscript a method for developing novel filtering algorithms through
the parallel concatenation of two Bayesian filters is illustrated. Our
description of this method, called turbo filtering, is based on a new graphical
model; this allows us to efficiently describe both the processing accomplished
inside each of the constituent filter and the interactions between them. This
model is exploited to develop two new filtering algorithms for conditionally
linear Gaussian systems. Numerical results for a specific dynamic system
evidence that such filters can achieve a better complexity-accuracy tradeoff
than marginalized particle filtering.
