Intelligent Localization of a High-Speed Train Using LSSVM and the Online Sparse Optimization Approach

For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 18; no. 8; pp. 2071 - 2084
Main Authors: Cheng, Ruijun, Song, Yongduan, Chen, Dewang, Chen, Long
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Algorithms
Computational modeling
Computer memory
Data models
Distance learning
High speed
High speed rail
High-speed train
Iterative methods
iterative pruning error minimization
Localization
location error
LSSVM
L₀-norm minimization
Machine learning
Mathematical model
online sparse optimization
Optimization
Pruning
Rail transportation
Real time
Real-time systems
Support vector machines
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L 0 -norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L 0 -norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai highspeed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the realtime performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2633344