Detail publikace

A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation

KROPFREITER, T. MEYER, F. HLAWATSCH, F.

Originální název

A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation

Anglický název

A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation

Jazyk

en

Originální abstrakt

We propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association. The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the performance is comparable to or better than that of the Gibbs sampler-based LMB filter.

Anglický abstrakt

We propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association. The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the performance is comparable to or better than that of the Gibbs sampler-based LMB filter.

Dokumenty

BibTex


@article{BUT170628,
  author="Thomas {Kropfreiter} and Florian {Meyer} and Franz {Hlawatsch}",
  title="A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation",
  annote="We propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association. The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the performance is comparable to or better than that of the Gibbs sampler-based LMB filter.",
  chapter="170628",
  doi="10.1109/TAES.2019.2941104",
  howpublished="online",
  number="3",
  volume="56",
  year="2019",
  month="september",
  pages="2478--2488",
  type="journal article in Web of Science"
}