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Title: Bayesian multiple extended target tracking using labelled random finite sets and splines
Authors: Daniyan, Abdullahi
Lambotharan, Sangarapillai
Deligiannis, Anastasios
Gong, Yu
Chen, Wen-Hua
Keywords: Multi-target tracking
Extended target tracking
Variational Bayesian
Poisson mixture
Random finite sets
Labelled random finite sets
GLMB Bernoulli filter
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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.
Abstract: 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.
Description: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor: 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.
Version: Published
DOI: 10.1109/TSP.2018.2873537
URI: https://dspace.lboro.ac.uk/2134/35425
Publisher Link: https://doi.org/10.1109/TSP.2018.2873537
ISSN: 1053-587X
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)
Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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