Measurement and analysis of train motion and railway track characteristics with inertial sensors

Heirich, Oliver; Lehner, Andreas; Robertson, Patrick; Strang, Thomas

doi:10.1109/itsc.2011.6082908

Cited by 53 publications

(38 citation statements)

References 3 publications

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“…Accelerometers are another example of an on-board sensor that can be used for this purpose, and Heirich et al [4] used accelerometers to derive speed profiles and detect track features, in this case measuring track curvature, changes in cant and changes in relative heading, instead of detecting switches and crossings as per Ref. [3].…”

Section: Introductionmentioning

confidence: 99%

Development and Comparison of Algorithms to Derive Vehicle Location from Speed Profile Data

Powell

Palacín

2018

Urban Rail Transit

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The rapid increases in the quantity of data being gathered regarding technological systems such as railways can promote improvements in their design and operation. Combining information from different datasets allows more in-depth analysis, such as using train location data to enhance the analysis of speed profiles and energy consumption. Positioning systems such as GPS are frequently used to obtain this information, but are not necessarily always available, such as in underground metro systems. The focus of this paper is therefore the development of algorithms to derive train location information from measured speed profile data and network topology. Two different algorithms were developed to extract individual station-to-station journeys from an example consisting of a dataset of speed profiles and energy consumption from an urban rail system, and four classification algorithms were developed to identify the station pairs associated with each journey. It was found that the best-performing approach for this task was to compare the cumulative distance of a group of several consecutive journeys against a database of station-to-station distances to find the best match. This was more resilient than constructing sequences of consecutive journeys from possible matches in a database of station-to-station distances and orders of magnitude faster than heuristic algorithms.

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Section: Introductionmentioning

confidence: 99%

Development and Comparison of Algorithms to Derive Vehicle Location from Speed Profile Data

Powell

Palacín

2018

Urban Rail Transit

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“…Through this approach, we are generating uniquely identifiable sensor characteristics as presented in a similar work [17], which then can be used to inversely detect position through matching sensor characteristics. While this is a very generous promise, the work presented goes so far as to prove the fact that a repeatable signal can be identified by using multiple train journeys.…”

Section: Signal Collection and Processingmentioning

confidence: 99%

Annotation and Position Recall from Low Grade Sensorial Data in the Context of Topological Railway Maps

Colceriu¹,

Stefanut²,

Bacu³

et al. 2017

STUD INFORM CONTROL

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Describing high quality track maps is an important prerequisite for the localization algorithms, which depend on relatively unreliable and unsafe triangulation technologies, such as the Global Navigation Satellite System solutions available on modern mobile devices. The state of the art studies and experiments concerning the maps annotation methodologies have been focused on using large quantities of crowdsourcing sensorial data and open source topological maps. The objective is to produce a low-cost platform for regional railway operators, to visualize the track's geometry and to generate a track representation, which can be then recalled across multiple journeys. The paper aims to develop and experiment with a sensorial signature representation toward providing a flexible solution for signal alignment based on Genetic Algorithms. The alignment solution intends to describe the sensorial signals of the track line in a relevant manner that makes localization algorithms possible based on the nonlinear state estimation.

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“…Inertial sensors are often used in combination with an integrated navigation system for GNSS position aiding, for example, in [6,7]. The yaw turn rate can also be used for the switch way identification that has been used in [8,9] and analyzed in [10,11]. Approaches with GNSS and IMU are found in [7][8][9] and extensions with eddy current sensor in [6,12,13].…”

Section: Related Workmentioning

confidence: 99%

Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map

Heirich

2016

Journal of Sensors

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Train localization is safety-critical and therefore the approach requires a continuous availability and a track-selective accuracy. A probabilistic approach is followed up in order to cope with multiple sensors, measurement errors, imprecise information, and hidden variables as the topological position within the track network. The nonlinear estimation of the train localization posterior is addressed with a novel Rao-Blackwellized particle filter (RBPF) approach. There, embedded Kalman filters estimate certain linear state variables while the particle distribution can cope with the nonlinear cases of parallel tracks and switch scenarios. The train localization algorithm is further based on a track map and measurements from a Global Navigation Satellite System (GNSS) receiver and an inertial measurement unit (IMU). The GNSS integration is loosely coupled and the IMU integration is achieved without the common strapdown approach and suitable for low-cost IMUs. The implementation is evaluated with real measurements from a regional train at regular passenger service over 230 km of tracks with 107 split switches and parallel track scenarios of 58.5 km. The approach is analyzed with labeled data by means of ground truth of the traveled switch way. Track selectivity results reach 99.3% over parallel track scenarios and 97.2% of correctly resolved switch ways.

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Measurement and analysis of train motion and railway track characteristics with inertial sensors

Cited by 53 publications

References 3 publications

Development and Comparison of Algorithms to Derive Vehicle Location from Speed Profile Data

Development and Comparison of Algorithms to Derive Vehicle Location from Speed Profile Data

Annotation and Position Recall from Low Grade Sensorial Data in the Context of Topological Railway Maps

Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map

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