Collaborative multiagent Gaussian inference in a dynamic environment using belief propagation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Particle Filtering for Large-Dimensional State Spaces With Multimodal Observation Likelihoods
IEEE Transactions on Signal Processing - Part I
Hi-index | 0.00 |
In [1], we introduced a novel distributed inference algorithm for the multiagent Gaussian inference problem, based on the framework of graphical models and message passing algorithms. We compare it to current state of the art techniques and we demonstrate that it is the most efficient one in terms of communication resources used. Moreover, we show experimentally that it outperforms the other methods in terms of estimation error on a general class of problems, even in presence of data loss.