Cooperative Joint Localization and Clock Synchronization Based on Gaussian Message Passing in Asynchronous Wireless Networks

TL;DR

This paper introduces distributed algorithms for joint localization and clock synchronization in wireless networks using Gaussian message passing on a factor graph, effectively handling non-line-of-sight measurements with reduced complexity.

Contribution

The paper develops a Gaussian message passing approach on a factor graph for joint localization and synchronization, linearizing nonlinear terms for efficiency, and proposes a schedule to balance performance and communication overhead.

Findings

Algorithms perform close to particle filtering methods

Significant reduction in computational complexity

Effective in densely connected networks

Abstract

Localization and synchronization are very important in many wireless applications such as monitoring and vehicle tracking. Utilizing the same time of arrival (TOA) measurements for simultaneous localization and synchronization is challenging. In this paper, we present a factor graph (FG) representation of the joint localization and time synchronization problem based on TOA measurements, in which the non-line-of-sight measurements are also taken into consideration. On this FG, belief propagation (BP) message passing and variational message passing (VMP) are applied to derive two fully distributed cooperative algorithms with low computational requirements. Due to the nonlinearity in the observation function, it is intractable to compute the messages in closed form and most existing solutions rely on Monte Carlo methods, e.g., particle filtering. We linearize a specific nonlinear term in the expressions of messages, which enables us to use a Gaussian representation for all messages. Accordingly, only the mean and variance have to be updated and transmitted between neighboring nodes, which significantly reduces the communication overhead and computational complexity. A message passing schedule scheme is proposed to trade off between estimation performance and communication overhead. Simulation results show that the proposed algorithms perform very close to particle-based methods with much lower complexity especially in densely connected networks.