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

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) representat...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 65; no. 9; pp. 7258 - 7273
Main Authors: Weijie Yuan, Nan Wu, Etzlinger, Bernhard, Hua Wang, Jingming Kuang
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Belief propagation (BP)
Clocks
Estimation
factor graph (FG)
Gaussian message passing
joint localization and synchronization
Localization
Message passing
message passing schedule
Nonlinear optics
Synchronization
variational message passing (VMP)
Wireless networks
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary: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 (NLOS) 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, particularly in densely connected networks.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2016.2518185