Zeitschriftenartikel:
O. Hlinka, F. Hlawatsch, P. Djuric:
"Distributed Particle Filtering in Agent Networks: A Survey, Classification, and Comparison";
IEEE Signal Processing Magazine,
30
(2013),
1;
S. 61
- 81.
Kurzfassung englisch:
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.
Schlagworte:
distributed particle filter, distributed state estimation, sequential Bayesian estimation, agent networks, target tracking
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/MSP.2012.2219652
Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/PubDat_216830.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.