Name
Abstract | ||
|---|---|---|
In this manuscript a novel online technique for Bayesian filtering, dubbed turbo filtering, is illustrated. In particular, it is shown that this filtering method, which can be interpreted as an extension of marginalized particle filtering, results from the application of the sum-product rule to a factor graph representing a mixed linear/nonlinear state-space model. Simulation results for a specific state-space model evidence that turbo filtering can outperform marginalized particle filtering in terms of both accuracy and complexity. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS) | State Space Representation, Hidden Markov Model, Marginalized Particle Filter, Belief Propagation, Turbo Processing |
Field | DocType | ISSN |
Turbo,Factor graph,Noise measurement,Computer science,Particle filter,Algorithm,Filter (signal processing),Hidden Markov model,Message passing,Belief propagation | Conference | 2164-7038 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. M. Vitetta | 1 | 232 | 27.47 |
| Emilio Sirignano | 2 | 0 | 0.68 |
| Francesco Montorsi | 3 | 12 | 3.51 |