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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.

In this paper, a factor graph approach is employed to investigate the recursive filtering problem for conditionally linear Gaussian state-space models. First, we derive a new factor graph for the considered filtering problem; then, we show that applying the sum-product rule to our graphical model results in both known and novel filtering techniques. In particular, we prove that: 1) marginalized particle filtering can be interpreted as a form of forward only message passing over the devised graph; 2) novel filtering methods can be easily developed by exploiting the graph structure and/or simplifying probabilistic messages.

In this paper, the problem of the joint estimation of the range and azimuth of multiple targets in a multiple-input multiple-output stepped-frequency continuous wave radar system is investigated. Three deterministic algorithms solving it through an iterative beam cancellation procedure are described; moreover, an iterative technique, based on the expectation-maximization algorithm, is developed with the aim of refining their estimates. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from a low power wideband radar device. Our results evidence that these algorithms achieve similar accuracies, but at the price of different computational efforts.