Publication detail

Likelihood Consensus 2.0: Reducing Interagent Communication in Distributed Bayesian Target Tracking

ŠAUŠA, E. RAJMIC, P. HLAWATSCH, F.

Original Title

Likelihood Consensus 2.0: Reducing Interagent Communication in Distributed Bayesian Target Tracking

Type

conference paper

Language

English

Original 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.

Keywords

Target tracking; distributed particle filter; Bayesian filtering; likelihood consensus; sparsity

Authors

ŠAUŠA, E.; RAJMIC, P.; HLAWATSCH, F.

Released

14. 4. 2024

Publisher

IEEE

Location

Soul

ISBN

979-8-3503-4485-1

Book

49th IEEE International Conference on Acoustics, Speech, and Signal Processing

Pages count

5

BibTex

@inproceedings{BUT197325,
  author="Erik {Šauša} and Pavel {Rajmic} and Franz {Hlawatsch}",
  title="Likelihood Consensus 2.0: Reducing Interagent Communication in Distributed Bayesian Target Tracking",
  booktitle="49th IEEE International Conference on Acoustics, Speech, and Signal Processing",
  year="2024",
  pages="5",
  publisher="IEEE",
  address="Soul",
  doi="10.1109/ICASSP48485.2024.10447108",
  isbn="979-8-3503-4485-1"
}