@Article{ChagasWald:2016:ThAnMe,
author = "Chagas, Ronan Arraes Jardim and Waldmann, Jacques",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Tecnol{\'o}gico de Aeron{\'a}utica (ITA)}",
title = "Theoretical analysis of the measurement transportation algorithm
to fuse delayed data in distributed sensor networks",
journal = "IEEE Transactions on Signal and Information Processing Over
Networks",
year = "2016",
volume = "2",
number = "3",
pages = "246--259",
month = "Sept",
keywords = "Measurement transportation (MT), delayed measurements, distributed
estimation, sensor network, Kalman filtering.",
abstract = "Distributed sensor networks are capable of robust dynamic system
estimation. The shared information in the network can prevent
significant degradation or the interruption of the estimation
process when a particular network node fails. However, the
estimation accuracy can be severely degraded if delayed
information is navely fused. The classical algorithm to fuse
delayed measurements in a distributed network is the reiterated
Kalman filter (RKF), which provides the optimal estimate in linear
and Gaussian systems. Nevertheless, this algorithm imposes a huge
computational burden and requires considerable memory when the
delay is large, thus precluding the use of RKF in embedded systems
that lack the needed computational resources. Previously, we
proposed a suboptimal algorithm called measurement transportation
(MT) that greatly reduces both thememory requirement and
computational burden and delivers accuracy comparable to that of
the RKF in a simulated UAV network. However, MT was only tested
with numerical simulations. Here, we extend the previous
investigation with the detailed analysis of MT regarding its
accuracy, memory necessity, and computational burden. Cases are
shown when the analysis predicts that the accuracy delivered by MT
is comparable to that of the RKF and the theoretical results are
then validated with a simulated distributed sensor network.",
doi = "10.1109/TSIPN.2016.2580461",
url = "http://dx.doi.org/10.1109/TSIPN.2016.2580461",
issn = "2373-776X",
language = "en",
targetfile = "chagas_theoretical.pdf",
urlaccessdate = "27 abr. 2024"
}