Publication detail

Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

ŠAUŠA, E. RAJMIC, P. HLAWATSCH, F.

Original Title

Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

Type

journal article in Web of Science

Language

English

Original Abstract

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.

Keywords

Target tracking; Particle filter; Likelihood consensus; Splines; Orthogonal matching pursuit; OMP; Sparsity; PDA filter

Authors

ŠAUŠA, E.; RAJMIC, P.; HLAWATSCH, F.

Released

1. 2. 2024

Publisher

Elsevier

ISBN

0165-1684

Periodical

SIGNAL PROCESSING

Year of study

215

Number

February 2024

State

Kingdom of the Netherlands

Pages from

1

Pages to

13

Pages count

13

URL

https://www.sciencedirect.com/science/article/pii/S016516842300333X

Full text in the Digital Library

http://hdl.handle.net/11012/214460

BibTex

@article{BUT184719,
  author="Erik {Šauša} and Pavel {Rajmic} and Franz {Hlawatsch}",
  title="Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0",
  journal="SIGNAL PROCESSING",
  year="2024",
  volume="215",
  number="February 2024",
  pages="1--13",
  doi="10.1016/j.sigpro.2023.109259",
  issn="0165-1684",
  url="https://www.sciencedirect.com/science/article/pii/S016516842300333X"
}