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Comparison of Time Warping Algorithms for Rail Vehicle Velocity Estimation in Low Speed Scenarios

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Precise measurement of rail vehicle velocities is an essential prerequisite for the implementation of modern train control systems and the improvement of transportation capacity and logistics. Novel eddy current sensor systems make it possible to estimate velocity by using cross-correlation techniques, which show a decline in precision in areas of high accelerations. This is due to signal distortions within the correlation interval. We propose to overcome these problems by employing algorithms from the field of dynamic programming. In this paper we evaluate the application of correlation optimized warping, an enhanced version of dynamic time warping algorithms, and compare it with the classical algorithm for estimating rail vehicle velocities in areas of high accelerations and decelerations.
Rocznik
Strony
161--173
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr., wzory
Twórcy
autor
  • University of Applied Sciences Offenburg, Department for Electrical Engineering, Badstraße 24, D-77652 Offenburg, Germany
  • Technical University of Sofia, Faculty of Electronic Engineering and Technologies, Kliment Ohridski Blvd., BG-1756 Sofia, Bulgaria
Bibliografia
  • [1] Winter, P., Braband J., de Cicco, P. (2009). Compendium on ERTMS: European Rail Traffic Management System. UIC International.
  • [2] Böhringer, F. (2003). Train location based on fusion of satellite and trainborne sensor data. Location Services and Navigation Technologies, 5084, 76-85.
  • [3] Engelberg, T., Mesch, F. (2000). Eddy current sensor system for non-contact speed and distance measurement of rail vehicles. Computers in Railways VII, 1261-1270.
  • [4] Bellman, R.E. (1957). Dynamic Programming. Princeton University Press, Princeton, New Jersey.
  • [5] Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. 26, 43-49.
  • [6] Tomasi, G., van den Berg, F., Anderson, C. (2004). Correlation optimized warping an dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemomertrics, 18, 231-241.
  • [7] Moll, H., Burkhardt, H. (1979). A modified Newton-Raphson-Search for the Model-Adaptive Identifications of Delays. Identification and System Parameter Estimation.
  • [8] Sadowski, J. (2014). Velocity Measurement using the Fdoa Method in Ground-Based Radio Navigation System. Metrol. Meas. Syst., 21(2), 363-376.
  • [9] Kowalczyk, A., Hanus, R., Szlachta, A. (2011). Investigation of the Statistical Method of Time Delay Estimation Based on Conditional Averaging of Delayed Signal. Metrol. Meas. Syst., 18(2), 335-342.
  • [10] McIntire, P., McMaster, R.C. (1986). Nondestructive Testing Handbook. The American Society for Nondestructive Testing, Columbus, Ohio.
  • [11] Hensel, S., Hasberg, C. (2008). HMM Based Segmentation of Continuous Eddy Current Sensor Signals. Proc. of the 11th IEEE International Conference on Intelligent Transportation Systems, 760-765.
  • [12] Papoulis, A., Unnikrishna Pillai, S. (2002). Probability, Random Variables, and Stochastic Processes. McGraw-Hill, Boston.
  • [13] Berger, C. (1998). Optische Korrelationssensoren zur Geschwindigkeitsmessung technischer Objekte. VDI Verlag.
  • [14] Kruskall, J., Liberman, M. (1983). The symmetric time-warping problem: from continuous to discrete. Time Warps, String Edits, and Mocromolecules: The Theory and Practice of Sequence Comparison, 125-161.
  • [15] Needleman, S., Wunsch, C. (1970). A general method applicable to the search for similarities in the amino acid sequences of two proteins. Journal of Molecular Biology, 443-453.
  • [16] Wang, C.P., Isenhour, T.L. (1987). Time-warping algorithm applied to chromatographic peak matching gas-chromatography Fourier-transform infrared mass-spectrometry. Anal.Chem., 59, 649-654.
  • [17] Reiner, E, Abbey, L.E., Moran, T.F., Papamichalis, P., Shafer, R.W. (1979). Characterization of normal human cells by pyrolysis gas-chromatography mass spectrometry. Biomedical mass spectrometry (Biomed Mass Spectrom), 6, 491-498.
  • [18] Nielsen, N. Carstensen, J., Smedsgaard, J. (1998). Aligning of single and multiple wavelength chromatoraphic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography A, A 805(1-2), 17-35.
  • [19] Bahlmann, C. (2003). Dynamic Time Warping techniques on an example of the on-line handwriting recognition. Department of Computer Science, Albert-Ludwigs-University Freiburg.
  • [20] Schmill, M., Oates, T., Cohen, P. (1999). Learned models for continuous planning. Seventh International Workshop on Artificial Intelligence and Statistics.
  • [21] Caiani, E. et al. (1998). Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume. IEEE Computers in Cardiology.
  • [22] Keogh, E., Pazzani, M. (2000). Derivative Dynamic Time Warping. Technical Report, University of California.
  • [23] Strauss, T., Hasberg, C., Hensel, S. (2009). Correlation based velocity estimation during acceleration phases with application in rail vehicles. IEEE/SP 15th Workshop on Statistical Signal Processing.
  • [24] Hensel, S., Strauss, T., Marinov, M. (2015). Eddy current sensor based velocity and distance estimation in rail vehicles. IET Science, Measurement & Technology, 9(7), 875-888.
  • [25] Hensel, S., Hasberg, C., Stiller, C. (2011). Probabilistic Rail Vehicle Localization with Eddy Current Sensors in Topological Maps. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1-13.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-857c599b-9ab8-4365-a59f-aa7206f2b973
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