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Bayesian multiple extended target tracking using labelled random finite sets and splines

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journal contribution
posted on 19.10.2018, 12:56 authored by Abdullahi Daniyan, Sangarapillai LambotharanSangarapillai Lambotharan, Anastasios Deligiannis, Yu GongYu Gong, Wen-Hua ChenWen-Hua Chen
In this paper, we propose a technique for the joint tracking and labelling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. In particular, we developed a Poisson mixture variational Bayesian (PMVB) model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1, the MOD University Defence Research Collaboration (UDRC) in Signal Processing UK and the Petroleum Technology Development Fund (PTDF), Nigeria.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Signal Processing

Volume

66

Issue

22

Pages

6076 - 6091

Citation

DANIYAN, A. ... et al, 2018. Bayesian multiple extended target tracking using labelled random finite sets and splines. IEEE Transactions on Signal Processing, 66(22), pp.6076-6091.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Acceptance date

14/09/2018

Publication date

2018-10-04

Notes

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.

ISSN

1053-587X

eISSN

1941-0476

Language

en