Talks and Poster Presentations (with Proceedings-Entry):
R. Repp, G. Papa, F. Meyer, P. Braca, F. Hlawatsch:
"A Distributed Bernoulli Filter Based on Likelihood Consensus with Adaptive Pruning";
Talk: 21st International Conference on Information Fusion (FUSION 2018),
Cambridge, UK;
07-10-2018
- 07-13-2018; in: "2018 21st International Conference on Information Fusion (FUSION)",
IEEE (ed.);
(2018),
ISBN: 978-0-9964527-6-2;
2445
- 2452.
English abstract:
The Bernoulli filter (BF) is a Bayes-optimal method for target tracking when the target can be present or absent in unknown time intervals and the measurements are affected by clutter and missed detections. We propose a distributed particle-based multisensor BF algorithm that approximates the centralized multisensor BF for arbitrary nonlinear and non-Gaussian system models. Our distributed algorithm uses a new extension of the likelihood consensus (LC) scheme that accounts for both target presence and absence and includes an adaptive pruning of the LC expansion coefficients. Simulation results for a heterogeneous sensor network with significant noise and clutter show that the performance of our algorithm is close to that of the centralized multisensor BF.
Keywords:
Bernoulli filter, distributed target tracking, distributed particle filtering, likelihood consensus, random finite set, sensor network
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.23919/ICIF.2018.8455302
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_277039.pdf
Created from the Publication Database of the Vienna University of Technology.