TDOA-Based Joint Synchronization and Localization Algorithm for Asynchronous Wireless Sensor Networks

This paper presents a joint synchronization and localization algorithm based on the time-difference-of-arrival (TDOA) for asynchronous Wireless Sensor Networks (WSNs), where the positions of anchors are relatively fixed but unknown. To obtain time synchronization and anchor positions, each anchor br...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 68; no. 5; pp. 3107 - 3124
Main Authors: Wang, Tan, Xiong, Hui, Ding, Hong, Zheng, Linhua
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Anchors
blink model
Clocks
Computer simulation
Covariance matrices
joint synchronization and localization algorithm
Localization
Lower bounds
Maximum likelihood estimation
Message passing
Monte Carlo simulation
Offsets
Parameter estimation
Random noise
relative clock offset and skew estimation
Sensors
Synchronization
Time synchronization
time-difference-of-arrival (TDOA)
Wireless networks
Wireless sensor network
Wireless sensor networks
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:This paper presents a joint synchronization and localization algorithm based on the time-difference-of-arrival (TDOA) for asynchronous Wireless Sensor Networks (WSNs), where the positions of anchors are relatively fixed but unknown. To obtain time synchronization and anchor positions, each anchor broadcasts signals periodically, while other anchors receive the broadcasting signals and stamp times-of-arrival (TOAs). Based on these TOAs, we estimate the internal clock parameters and anchor positions in three steps: least square estimation (LSE) of the relative clock skew based on TDOAs, maximum likelihood localization (MLE) of anchors using biased time of flight (TOF), and LSE estimation of relative clock offsets. Anchor pairs stamp the TDOAs when a tag (a wireless sensor node that requires localization) transmits a signal. The biased TDOAs, due to asynchronous local clocks, are compensated by relative clock offset estimation. The maximum likelihood estimation is used for the tag localization based on the Gaussian noise model. We evaluate the performance of the proposed synchronization and localization algorithm using the Cramér-Rao lower bound (CRLB). Simulations are carried out to verify the validity of the algorithm in this paper.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2020.2973961