A Review on Kernel Learning Method of Moving Target Tracking

The kernel method maps the original spatial data to a high-dimensional Hilbert space by nonlinear mapping and hides the mapping in the linear learner. The kernel function is used to replace the complex inner product operation in high-dimensional space, which can effectively avoid the ‘cu...

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Autor principal:	Lou Jiaxin, Li Yuankai, Wang Yuan, Xu Yanke
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|kernel learning method\|nonlinear mapping\|target detection\|target tracking\|multiple kernel learning\|pattern recognition Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/5368972fe3b84128a900f037435fc3b7
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id	oai:doaj.org-article:5368972fe3b84128a900f037435fc3b7
record_format	dspace
spelling	oai:doaj.org-article:5368972fe3b84128a900f037435fc3b72021-11-30T00:13:23ZA Review on Kernel Learning Method of Moving Target Tracking1673-504810.12132/ISSN.1673-5048.2021.0030https://doaj.org/article/5368972fe3b84128a900f037435fc3b72021-10-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2021-00030.pdfhttps://doaj.org/toc/1673-5048The kernel method maps the original spatial data to a high-dimensional Hilbert space by nonlinear mapping and hides the mapping in the linear learner. The kernel function is used to replace the complex inner product operation in high-dimensional space, which can effectively avoid the ‘curse of dimensionality’ caused by high-dimensional space calculation. The kernel method has the advantages of learnability, efficient calculation, linearization and good generalization performance, which provides a new effective way to solve the problem of nonlinear target tracking. The traditional target tracking methods often use the tracking model to predict the current motion state of the target and ensure the accuracy and real-time tracking. The kernel method provides a general way of linearization and can be independent of the specific model with efficient computing. Introducing the kernel learning method into target tracking is expected to improve environmental adaptability. In this paper, based on the idea of kernel method, the current research progress of kernel learning target tracking is presented, including target detection method based on kernel learning, generative and discriminative target tracking method, and multi-kernel learning method with different kernel functions. Further research on kernel learning target tracking for kernel function optimization, long-term tracking, feature extraction and target occlusion are prospected.Lou Jiaxin, Li Yuankai, Wang Yuan, Xu YankeEditorial Office of Aero Weaponryarticle\|kernel learning method\|nonlinear mapping\|target detection\|target tracking\|multiple kernel learning\|pattern recognitionMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 5, Pp 64-75 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|kernel learning method\|nonlinear mapping\|target detection\|target tracking\|multiple kernel learning\|pattern recognition Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|kernel learning method\|nonlinear mapping\|target detection\|target tracking\|multiple kernel learning\|pattern recognition Motor vehicles. Aeronautics. Astronautics TL1-4050 Lou Jiaxin, Li Yuankai, Wang Yuan, Xu Yanke A Review on Kernel Learning Method of Moving Target Tracking
description	The kernel method maps the original spatial data to a high-dimensional Hilbert space by nonlinear mapping and hides the mapping in the linear learner. The kernel function is used to replace the complex inner product operation in high-dimensional space, which can effectively avoid the ‘curse of dimensionality’ caused by high-dimensional space calculation. The kernel method has the advantages of learnability, efficient calculation, linearization and good generalization performance, which provides a new effective way to solve the problem of nonlinear target tracking. The traditional target tracking methods often use the tracking model to predict the current motion state of the target and ensure the accuracy and real-time tracking. The kernel method provides a general way of linearization and can be independent of the specific model with efficient computing. Introducing the kernel learning method into target tracking is expected to improve environmental adaptability. In this paper, based on the idea of kernel method, the current research progress of kernel learning target tracking is presented, including target detection method based on kernel learning, generative and discriminative target tracking method, and multi-kernel learning method with different kernel functions. Further research on kernel learning target tracking for kernel function optimization, long-term tracking, feature extraction and target occlusion are prospected.
format	article
author	Lou Jiaxin, Li Yuankai, Wang Yuan, Xu Yanke
author_facet	Lou Jiaxin, Li Yuankai, Wang Yuan, Xu Yanke
author_sort	Lou Jiaxin, Li Yuankai, Wang Yuan, Xu Yanke
title	A Review on Kernel Learning Method of Moving Target Tracking
title_short	A Review on Kernel Learning Method of Moving Target Tracking
title_full	A Review on Kernel Learning Method of Moving Target Tracking
title_fullStr	A Review on Kernel Learning Method of Moving Target Tracking
title_full_unstemmed	A Review on Kernel Learning Method of Moving Target Tracking
title_sort	review on kernel learning method of moving target tracking
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/5368972fe3b84128a900f037435fc3b7
work_keys_str_mv	AT loujiaxinliyuankaiwangyuanxuyanke areviewonkernellearningmethodofmovingtargettracking AT loujiaxinliyuankaiwangyuanxuyanke reviewonkernellearningmethodofmovingtargettracking
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A Review on Kernel Learning Method of Moving Target Tracking

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