Train Localization Using an Adaptive Multisensor Data Fusion Technique

Malakar, Bidhan; Roy, Binoy Krishna

doi:10.3846/transport.2019.11313

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…That study focused on solutions that could be used on every rail vehicle regardless of the ground equipment on the specific lines. In another study [62], an adaptive multi-sensor data fusion technique for the precise assessment of the train's position and velocity, based on three on-board sensors-longitudinal accelerometer, odometer, and GPS receiver unit-is suggested. A localisation technique for railway vehicles, based on the fusion of tachometer and IMU and with the aim of performance enhancement in terms of speed and position estimation accuracy, is introduced in [63].…”

Section: On-board Sensorsmentioning

confidence: 99%

“…A localisation algorithm for increasing the accuracy of the odometric estimation, especially in critical adhesion conditions, based on sensor fusion between tachometers and inertial measurement unit, is suggested in [84]. A data fusion technique is proposed in [62] for a train localisation system consisting of three on-board sensors, namely, longitudinal accelerometer, odometer, and GPS receiver unit. Wang et al [124] proposed a train positioning method which fuses vison and millimeterwave radar data.…”

Section: Sensor Fusion Algorithms For Vehicle-based Localisation On T...mentioning

confidence: 99%

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A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems

Rahimi

Liu

Durazo-Cardenas

et al. 2022

Sensors

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Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle’s position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.

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Section: On-board Sensorsmentioning

confidence: 99%

Section: Sensor Fusion Algorithms For Vehicle-based Localisation On T...mentioning

confidence: 99%

A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems

Rahimi

Liu

Durazo-Cardenas

et al. 2022

Sensors

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Truth-Correction-Based Multisensor Fusion Method for HTS Maglev Train’s Position Detection

Jiang,

Wang,

Liang

et al. 2024

IEEE Trans. Instrum. Meas.

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RETRACTED ARTICLE: Implementation of MapReduce parallel computing framework based on multi-data fusion sensors and GPU cluster

Chang

et al. 2021

EURASIP J. Adv. Signal Process.

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Nowadays, with the rapid growth of data volume, massive data has become one of the factors that plague the development of enterprises. How to effectively process data and reduce the concurrency pressure of data access has become the driving force for the continuous development of big data solutions. This article mainly studies the MapReduce parallel computing framework based on multiple data fusion sensors and GPU clusters. This experimental environment uses a Hadoop fully distributed cluster environment, and the entire programming of the single-source shortest path algorithm based on MapReduce is implemented in Java language. 8 ordinary physical machines are used to build a fully distributed cluster, and the configuration environment of each node is basically the same. The MapReduce framework divides the request job into several mapping tasks and assigns them to different computing nodes. After the mapping process, a certain intermediate file that is consistent with the final file format is generated. At this time, the system will generate several reduction tasks and distribute these files to different cluster nodes for execution. This experiment will verify the changes in the running time of the PSON algorithm when the size of the test data set gradually increases while keeping the hardware level and software configuration of the Hadoop platform unchanged. When the number of computing nodes increases from 2 to 4, the running time is significantly reduced. When the number of computing nodes continues to increase, the reduction in running time will become less and less significant. The results show that NESTOR can complete the basic workflow of MapReduce, and simplifies the process of user development of GPU positive tree order, which has a significant speedup for applications with large amounts of calculations.

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Train Localization Using an Adaptive Multisensor Data Fusion Technique

Cited by 3 publications

References 23 publications

A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems

A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems

Truth-Correction-Based Multisensor Fusion Method for HTS Maglev Train’s Position Detection

RETRACTED ARTICLE: Implementation of MapReduce parallel computing framework based on multi-data fusion sensors and GPU cluster

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