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.

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)

Electronic version of the publication:

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