Digital twins based intelligent state prediction method for maneuvering-target tracking

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dc.contributor.authorLiu, Jingxian
dc.contributor.authorYan, Junjie
dc.contributor.authorWan, Dehuan
dc.contributor.authorLi, Xuran
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorAl-Dulaimi, Anwer
dc.contributor.authorQuan, Zhi
dc.date.accessioned2023-09-15T13:17:39Z
dc.date.available2023-09-15T13:17:39Z
dc.date.issued2023-08-30
dc.description.abstractManeuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.en_UK
dc.identifier.citationLiu J, Yan J, Wan D, et al., (2023) Digital twins based intelligent state prediction method for maneuvering-target tracking. IEEE Journal on Selected Areas in Communications, Volume 41, Issue 11, November 2023, pp. 3589-3606en_UK
dc.identifier.issn0733-8716
dc.identifier.urihttps://doi.org/10.1109/JSAC.2023.3310109
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20219
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectManeuvering-target Trackingen_UK
dc.subjectDigital Twinsen_UK
dc.subjectNeural Networken_UK
dc.titleDigital twins based intelligent state prediction method for maneuvering-target trackingen_UK
dc.typeArticleen_UK

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