Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
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;
10.06.2018
- 13.06.2018; in: "2018 IEEE Statistical Signal Processing Workshop (SSP)",
IEEE (Hrg.);
(2018),
ISBN: 978-1-5386-1571-3;
S. 653
- 657.
Kurzfassung englisch:
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.
Schlagworte:
distributed target tracking, sensor network, probabilistic data association, likelihood consensus, orthogonal matching pursuit
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/SSP.2018.8450815
Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_277016.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.