  We study the problem of high accuracy localization of mobile nodes in a
multipath-rich environment where sub-meter accuracies are required. We employ a
peer-to-peer framework where the vehicles/nodes can get pairwise
multipath-degraded ranging estimates in local neighborhoods together with a
fixed number of anchor nodes. The challenge is to overcome the
multipath-barrier with redundancy in order to provide the desired accuracies
especially under severe multipath conditions when the fraction of received
signals corrupted by multipath is dominating. We invoke a analytical graphical
model framework based on particle filtering and reveal its high accuracy
localization promise through simulations. We also address design questions such
as "How many anchors and what fraction of line-of-sight (LOS) measurements are
needed to achieve a specified target accuracy?", by analytically characterizing
the performance improvement in localization accuracy as a function of the
number of nodes in the network and the fraction of LOS measurements. In
particular, for a static node placement, we show that the Cramer-Rao Lower
Bound (CRLB), a fundamental lower bound on the localization accuracy, can be
expressed as a product of two factors - a scalar function that depends only on
the parameters of the noise distribution and a matrix that depends only on the
geometry of node locations and the underlying connectivity graph. Further, a
simplified expression is obtained for the CRLB that helps deduce the scaling
behavior of the estimation error as a function of the number of agents and
anchors in the network. The bound suggests that even a small fraction of LOS
measurements can provide significant improvements. Conversely, a small fraction
of NLOS measurements can significantly degrade the performance. The analysis is
extended to the mobile setting and the performance is compared with the derived
CRLB.
