Target Tracking Using a Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127651" target="_blank" >RIV/00216305:26220/18:PU127651 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8450815" target="_blank" >https://ieeexplore.ieee.org/document/8450815</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSP.2018.8450815" target="_blank" >10.1109/SSP.2018.8450815</a>

Result language
angličtina
Original language name
Target Tracking Using a Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus
Original language description
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.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2018 IEEE Statistical Signal Processing Workshop (SSP)
ISBN
978-1-5386-1570-6
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
653-657
Publisher name
IEEE
Place of publication
Freiburg im Breisgau
Event location
Freiburg im Breisgau
Event date
Jun 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000720116000132

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