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D-SLATS: Distributed Simultaneous Localization and Time Synchronization
Through the last decade, we have witnessed a surge of Internet of Things
(IoT) devices, and with that a greater need to choreograph their actions across
both time and space. Although these two problems, namely time synchronization
and localization, share many aspects in common, they are traditionally treated
separately or combined on centralized approaches that results in an ineffcient
use of resources, or in solutions that are not scalable in terms of the number
of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three
different and independent algorithms to jointly solve time synchronization and
localization problems in a distributed fashion. The First two algorithms are
based mainly on the distributed Extended Kalman Filter (EKF) whereas the third
one uses optimization techniques. No fusion center is required, and the devices
only communicate with their neighbors. The proposed methods are evaluated on
custom Ultra-Wideband communication Testbed and a quadrotor, representing a
network of both static and mobile nodes. Our algorithms achieve up to three
microseconds time synchronization accuracy and 30 cm localization error
Distributed localization of a RF target in NLOS environments
We propose a novel distributed expectation maximization (EM) method for
non-cooperative RF device localization using a wireless sensor network. We
consider the scenario where few or no sensors receive line-of-sight signals
from the target. In the case of non-line-of-sight signals, the signal path
consists of a single reflection between the transmitter and receiver. Each
sensor is able to measure the time difference of arrival of the target's signal
with respect to a reference sensor, as well as the angle of arrival of the
target's signal. We derive a distributed EM algorithm where each node makes use
of its local information to compute summary statistics, and then shares these
statistics with its neighbors to improve its estimate of the target
localization. Since all the measurements need not be centralized at a single
location, the spectrum usage can be significantly reduced. The distributed
algorithm also allows for increased robustness of the sensor network in the
case of node failures. We show that our distributed algorithm converges, and
simulation results suggest that our method achieves an accuracy close to the
centralized EM algorithm. We apply the distributed EM algorithm to a set of
experimental measurements with a network of four nodes, which confirm that the
algorithm is able to localize a RF target in a realistic non-line-of-sight
scenario.Comment: 30 pages, 11 figure
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