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Publication detail
REPP, R. RAJMIC, P. MEYER, F. HLAWATSCH, F.
Original Title
Target Tracking Using a Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus
Type
conference paper
Language
English
Original 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
Authors
REPP, R.; RAJMIC, P.; MEYER, F.; HLAWATSCH, F.
Released
11. 6. 2018
Publisher
IEEE
Location
Freiburg im Breisgau
ISBN
978-1-5386-1570-6
Book
Proceedings of the 2018 IEEE Statistical Signal Processing Workshop (SSP)
Pages from
653
Pages to
657
Pages count
5
URL
https://ieeexplore.ieee.org/document/8450815
BibTex
@inproceedings{BUT147019, author="Rene {Repp} and Pavel {Rajmic} and Florian {Meyer} and Franz {Hlawatsch}", title="Target Tracking Using a Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus", booktitle="Proceedings of the 2018 IEEE Statistical Signal Processing Workshop (SSP)", year="2018", pages="653--657", publisher="IEEE", address="Freiburg im Breisgau", doi="10.1109/SSP.2018.8450815", isbn="978-1-5386-1570-6", url="https://ieeexplore.ieee.org/document/8450815" }