Bayesian multiple extended target tracking using labelled random finite sets and splines
journal contributionposted on 2018-10-19, 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.
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.
- Mechanical, Electrical and Manufacturing Engineering
Published inIEEE Transactions on Signal Processing
Pages6076 - 6091
CitationDANIYAN, 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.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
- VoR (Version of Record)
Publisher statementThis 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/
NotesThis work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.