Name
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 pa... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSP.2019.2893868 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Graphical models,Message passing,Covariance matrices,Hidden Markov models,Bayes methods,Computational modeling,Filtering | Factor graph,State vector,Particle filter,Filter (signal processing),Theoretical computer science,Filtering problem,Gaussian,State variable,Mathematics,Message passing | Journal |
Volume | Issue | ISSN |
67 | 6 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giorgio M. Vitetta | 1 | 27 | 4.90 |
| Emilio Sirignano | 2 | 0 | 0.68 |
| Francesco Montorsi | 3 | 28 | 3.16 |
| Matteo Sola | 4 | 2 | 0.72 |
| Pasquale Di Viesti | 5 | 0 | 0.68 |