Abstract
We propose a hybrid message passing method
for distributed cooperative localization and tracking of mobile
agents. Belief propagation and mean field message passing are
employed for, respectively, the motion-related and measurementrelated
part of the factor graph. Using a Gaussian belief approximation,
only three real values per message passing iteration
have to be broadcast to neighboring agents. Despite these very
low communication requirements, the estimation accuracy can
be comparable to that of particle-based belief propagation.
for distributed cooperative localization and tracking of mobile
agents. Belief propagation and mean field message passing are
employed for, respectively, the motion-related and measurementrelated
part of the factor graph. Using a Gaussian belief approximation,
only three real values per message passing iteration
have to be broadcast to neighboring agents. Despite these very
low communication requirements, the estimation accuracy can
be comparable to that of particle-based belief propagation.
| Original language | English |
|---|---|
| Journal | I E E E Signal Processing Letters |
| Volume | 23 |
| Issue number | 6 |
| Pages (from-to) | 828 - 832 |
| ISSN | 1070-9908 |
| DOIs | |
| Publication status | Published - 5 Apr 2016 |
Keywords
- Belief propagation
- mean field approximation
- cooperative localization
- distributed estimation
- information projection
- Kullback-Leibler-divergence,
- mobile agent network