<div class="csl-bib-body">
<div class="csl-entry">Šauša, E., Rajmic, P., & Hlawatsch, F. (2024). Likelihood Consensus 2.0: Reducing Interagent Communication in Distributed Bayesian Target Tracking. In <i>ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i> (pp. 13006–13010). IEEE. https://doi.org/10.1109/ICASSP48485.2024.10447108</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/199768
-
dc.description.abstract
We propose a communication-efficient scheme for distributed Bayesian target tracking (distributed particle filtering) in possibly nonlinear and non-Gaussian state-space models. The scheme is a sparsity-promoting evolution of the likelihood consensus (LC) that uses the orthogonal matching pursuit (OMP), a B-spline dictionary, a distributed adaptive determination of the relevant state-space region, and an efficient binary representation of the LC expansion coefficients. Our simulation results show that a reduction of interagent communication by a factor of about 190 can be obtained without compromising the tracking performance.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
-
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Bayesian filtering
en
dc.subject
distributed particle filter
en
dc.subject
likelihood consensus
en
dc.subject
sparsity
en
dc.subject
Target tracking
en
dc.title
Likelihood Consensus 2.0: Reducing Interagent Communication in Distributed Bayesian Target Tracking
