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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 nodes can get pairwise multipath-degraded ranging estimates in local neighborhoods, with the multipath noise correlated across time. The challenge is to enable high-accuracy positioning under severe multipath conditions when the fraction of received signals corrupted by multipath is significant. Our contributions are two-fold. We provide a practical distributed localization algorithm by invoking an analytical graphical model framework based on particle filtering and validate its potential for high accuracy localization through simulations. In a practical DSRC mobile simulation setup, we show that the algorithm can achieve errors < 1m 90% of the time even when the fraction of line-ofsight signals is less than 35%. We also address design questions such as How many anchors and what fraction of line-of-sight measurements are needed to achieve a specified target accuracy? by showing that the Cramer-Rao Lower Bound for localization 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. A simplified expression is obtained that provides an insightful understanding of the bound and helps deduce the scaling behavior of the estimation error as a function of the number of agents and anchors in the network.