Collaborative High-Accuracy Localization in Mobile Multipath Environments
We study the problem of high-accuracy localization of mobile nodes in a multipath-rich environment where submeter accuracy values 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 multiple paths is significant. Our contributions are twofold. We provide a practical distributed localization algorithm by invoking an analytical graphical model framework based on particle filtering, and we validate its potential for high-accuracy localization through simulations. In a practical dedicated short-range communication (DSRC) mobile simulation setup, we show that the algorithm can achieve errors of <; 1 m 90% of the time, even when the fraction of line-of-sight (LOS) signals is less than 35%. We also address design questions such as “how many anchors and what fraction of LOS measurements are needed to achieve a specified target accuracy?” by showing that the Cramer-Rao lower bound (CRLB) 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 that helps deduce the scaling behavior of the estimation error as a function of the number of agents and anchors in the network.