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.

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)

Electronic version of the publication:

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