Talks and Poster Presentations (with Proceedings-Entry):
R. Repp, P. Rajmic, F. Meyer, F. Hlawatsch:
"Target Tracking Using a Distributed Particle-PDA Filter With Sparsity-Promoting Likelihood Consensus";
Poster: 2018 IEEE Statistical Signal Processing Workshop (SSP),
Freiburg im Breisgau, Germany;
06-10-2018
- 06-13-2018; in: "2018 IEEE Statistical Signal Processing Workshop (SSP)",
IEEE (ed.);
(2018),
ISBN: 978-1-5386-1571-3;
653
- 657.
English abstract:
We propose a distributed particle-based probabilistic data association filter (PDAF) for target tracking in the presence of clutter and missed detections. The proposed PDAF employs a new "sparsity-promoting" likelihood consensus that uses the orthogonal matching pursuit for a sparse approximation of the local likelihood functions. Simulation results demonstrate that, compared to the conventional likelihood consensus based on least-squares approximation, large savings in intersensor communication can be obtained without compromising the tracking performance.
Keywords:
distributed target tracking, sensor network, probabilistic data association, likelihood consensus, orthogonal matching pursuit
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/SSP.2018.8450815
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_277016.pdf
Created from the Publication Database of the Vienna University of Technology.