Contributions to Proceedings:
O. Hlinka, O. Sluciak, F. Hlawatsch, M. Rupp:
"Distributed data fusion using iterative covariance intersection";
in: "Ieee Icassp 2014",
IEEE - Institute of Electrical and Electronics Engineers, Inc.,
We propose an iterative extension of the covariance intersection
(CI) algorithm for distributed data fusion. Our iterative CI (ICI) algorithm is able to disseminate local information throughout the network. We show that the ICI algorithm converges asymptotically to
a consensus across all network nodes. We furthermore apply the
ICI algorithm to distributed sequential Bayesian estimation and propose an ICI-based distributed particle filter (DPF). This DPF allows for spatially correlated measurement noises with unknown crosscorrelations and does not require knowledge of the network size.
The performance of the proposed DPF is assessed experimentally
for a target tracking problem.
Distributed data fusion, covariance intersection, distributed estimation, distributed particle filter, sensor network
Electronic version of the publication:
Project Head Markus Rupp:
Signal and Information Processing in Science and Engineering II: Theory and Implementation of Distributed Algorithms
Created from the Publication Database of the Vienna University of Technology.