Publications in Scientific Journals:

O. Hlinka, F. Hlawatsch, P. Djuric:
"Distributed Particle Filtering in Agent Networks: A Survey, Classification, and Comparison";
IEEE Signal Processing Magazine, 30 (2013), 1; 61 - 81.

English abstract:
Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents in order to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation problems that often occur in applications involving agent networks (ANs). In this article, we present a survey, classification, and comparison of various DPF approaches and algorithms available to date. Our emphasis is on decentralized ANs that do not include a central processing or control unit.

distributed particle filter, distributed state estimation, sequential Bayesian estimation, agent networks, 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.