Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning

The speed sensor fusion of urban rail transit train speed ranging based on deep learning builds a user-friendly structure but it in-turn increases the risk of traffic that significantly challenges its safety and transportation efficacy. In order to improve the operation safety and transportation eff...

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Autores principales:	Zhan Xuemei, Mu Zhong Hua, Kumar Rajeev, Shabaz Mohammad
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	information fusion light speed sensor the train velocity ranging Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/eb106c6494ac4537bd94a6a3a98f7d87
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id	oai:doaj.org-article:eb106c6494ac4537bd94a6a3a98f7d87
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spelling	oai:doaj.org-article:eb106c6494ac4537bd94a6a3a98f7d872021-12-05T14:10:57ZResearch on speed sensor fusion of urban rail transit train speed ranging based on deep learning2192-80102192-802910.1515/nleng-2021-0028https://doaj.org/article/eb106c6494ac4537bd94a6a3a98f7d872021-11-01T00:00:00Zhttps://doi.org/10.1515/nleng-2021-0028https://doaj.org/toc/2192-8010https://doaj.org/toc/2192-8029The speed sensor fusion of urban rail transit train speed ranging based on deep learning builds a user-friendly structure but it in-turn increases the risk of traffic that significantly challenges its safety and transportation efficacy. In order to improve the operation safety and transportation efficiency of urban rail transit trains, a train speed ranging system based on embedded multi-sensor information is proposed in this article. The status information of the train is acquired by the axle speed sensor and the Doppler radar speed sensor; however, the query transponder collects the status information of the train, and is used in the embedded system. Various other modules like adaptive correction, idling/sliding detection and compensation of speed transition/sliding are used in the proposed methodology to reduce the vehicle speed positioning errors due to factors such as wheel wear, idling, sliding, and environment. The results show that the running time of the train is 1000s, the output period of the axle speed sensor is 0.005s and the accelerometer output period is 0.01s. The output cycle of doppler radar is observed to be 0.1s, the output cycle of the transponder is 1s and the fusion period of the main filter is observed as 1s. The train speed ranging system of the embedded multi-sensor information fusion system proposed in this article can effectively improve the accuracy of the train speed positioning.Zhan XuemeiMu Zhong HuaKumar RajeevShabaz MohammadDe Gruyterarticleinformation fusionlight speed sensorthe trainvelocity rangingEngineering (General). Civil engineering (General)TA1-2040ENNonlinear Engineering, Vol 10, Iss 1, Pp 363-373 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	information fusion light speed sensor the train velocity ranging Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	information fusion light speed sensor the train velocity ranging Engineering (General). Civil engineering (General) TA1-2040 Zhan Xuemei Mu Zhong Hua Kumar Rajeev Shabaz Mohammad Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
description	The speed sensor fusion of urban rail transit train speed ranging based on deep learning builds a user-friendly structure but it in-turn increases the risk of traffic that significantly challenges its safety and transportation efficacy. In order to improve the operation safety and transportation efficiency of urban rail transit trains, a train speed ranging system based on embedded multi-sensor information is proposed in this article. The status information of the train is acquired by the axle speed sensor and the Doppler radar speed sensor; however, the query transponder collects the status information of the train, and is used in the embedded system. Various other modules like adaptive correction, idling/sliding detection and compensation of speed transition/sliding are used in the proposed methodology to reduce the vehicle speed positioning errors due to factors such as wheel wear, idling, sliding, and environment. The results show that the running time of the train is 1000s, the output period of the axle speed sensor is 0.005s and the accelerometer output period is 0.01s. The output cycle of doppler radar is observed to be 0.1s, the output cycle of the transponder is 1s and the fusion period of the main filter is observed as 1s. The train speed ranging system of the embedded multi-sensor information fusion system proposed in this article can effectively improve the accuracy of the train speed positioning.
format	article
author	Zhan Xuemei Mu Zhong Hua Kumar Rajeev Shabaz Mohammad
author_facet	Zhan Xuemei Mu Zhong Hua Kumar Rajeev Shabaz Mohammad
author_sort	Zhan Xuemei
title	Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
title_short	Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
title_full	Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
title_fullStr	Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
title_full_unstemmed	Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
title_sort	research on speed sensor fusion of urban rail transit train speed ranging based on deep learning
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/eb106c6494ac4537bd94a6a3a98f7d87
work_keys_str_mv	AT zhanxuemei researchonspeedsensorfusionofurbanrailtransittrainspeedrangingbasedondeeplearning AT muzhonghua researchonspeedsensorfusionofurbanrailtransittrainspeedrangingbasedondeeplearning AT kumarrajeev researchonspeedsensorfusionofurbanrailtransittrainspeedrangingbasedondeeplearning AT shabazmohammad researchonspeedsensorfusionofurbanrailtransittrainspeedrangingbasedondeeplearning
_version_	1718371576061100032

Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning

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