Multiphysical System of Operational Monitoring of the Condition of the Railway Track

Larionov, Daniil Yu.; Romantsova, Natalia V.; Shalymov, Roman V.

doi:10.1109/icite.2019.8880225

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Control surveys employ various survey technologies using, e.g., mobile laser scanning [7], axle box acceleration devices [8], trolley system and total station [9], global navigation satellite system (GNSS) observations [10,11], inertial measurement units [12], and vision systems [13]. The literature also describes advanced methods for connecting multiple sensors on a single survey platform [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Adjustment of GNSS Railway Measurements with Parameter-Binding Conditions in a Stationary Scenario

Dąbrowski

Specht

et al. 2022

Applied Sciences

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The study aims to assess the applicability of the ordinary least squares method, robust estimation, and conditions-binded adjustment in processing the six synchronous coordinate pairs of global navigation satellite system (GNSS) receivers. The research is part of the research project InnoSatTrack, focused on the enhancement of the determination of geometrical parameters of railway tracks using GNSS, inertial, and other sensors. A fixed frame of installation of six receivers is used in the data processing. Due to the limited availability of railway routes for measurements, the stationary campaign was conducted to gather data for the adjustment procedures tests. Six GNSS receivers in the frame-restricted configuration were set up using the tachymetric method for recording 96,729 synchronous static observations. Post-processed coordinates of the receivers were adjusted according to the geometrical conditions of the survey frame. Analysis showed higher applicability of the conditions-binded method than other estimation methods by obtaining superior results in both the precision and the maintenance of the design conditions factors than in the OLS and robust estimators.

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