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.
Vitetta, G.M., Sirignano, E., Di Viesti, P., Montorsi, F., Sola, M. (2019). Marginalized Particle Filtering and Related Filtering Techniques as Message Passing. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 67(6), 1522-1536 [10.1109/TSP.2019.2893868].
Marginalized Particle Filtering and Related Filtering Techniques as Message Passing
Di Viesti, Pasquale;
2019
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
