  We proposed a fusion mechanism for the distributed cooperative map matching
(CMM) within the vehicular ad-hoc network. This mechanism makes the information
from each node reachable within the network by other nodes without direct
communication, thus improving the overall localization accuracy and robustness.
Each node runs a Rao-Blackwellized particle filter (RBPF) that processes the
Global Navigation Satellite System (GNSS) measurements of its own and its
neighbors, followed by a map matching step that reduces or eliminates the GNSS
atmospheric error. Then each node fuses its own filtered results with those
from its neighbors for a better estimation. In this work, the complicated
dynamics and fusion mechanics of these RBPFs are represented by a linear
dynamical system. We proposed a distributed optimization framework that
explores the model to improve both robustness and accuracy of the distributed
CMM. The effectiveness of this distributed optimization framework is
illustrated by simulation results on realistic vehicular networks drawn from
data, compared with the centralized one and a decentralized one with random
fusion weights.
