Publications in Scientific Journals:

O. Hlinka, O. Sluciak, F. Hlawatsch, P. Djuric, M. Rupp:
"Likelihood Consensus and Its Application to Distributed Particle Filtering";
IEEE Transactions on Signal Processing, 60 (2012), 8; 4334 - 4349.

English abstract:
We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task-based on the past and current measurements of all sensors-using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms.
This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional
probability density functions of the local measurements) belong to
the exponential family of distributions.We then use the likelihood
consensus method to implement a distributed particle filter and
a distributed Gaussian particle filter. Each sensor runs a local
particle filter, or a local Gaussian particle filter, that computes a
global state estimate. The weight update in each local (Gaussian)
particle filter employs the JLF, which is obtained through the
likelihood consensus scheme. For the distributed Gaussian particle
filter, the number of particles can be significantly reduced by
means of an additional consensus scheme. Simulation results are
presented to assess the performance of the proposed distributed
particle filters for a multiple target tracking problem.

wireless sensor network, distributed state estimation, sequential Bayesian estimation, consensus algorithm, distributed particle filter, distributed Gaussian particle filter, target tracking

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.