| Peer-Reviewed

Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm

Received: 4 June 2017     Accepted: 26 June 2017     Published: 10 August 2017
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Abstract

Locomotive surveillance is the most active research topic and still faces big technical challenges in railway safety control system. An end-to-end locomotive tracking and continuous monitoring system is necessary for safety measures in satellite visible and low satellite visible environment. These smart systems aim to updates the information on location, exact detection, speed limitation and also rail track information. This paper contributes to develop an intelligent tracking and monitoring system based on Internet of Things (IoT) platform using Differential Global Positioning System (DGPS) for improved tracking accuracy of locomotive in both environments. Interacting Multiple Model (IMM) tracking algorithm based on Di-filter model is proposed for analysis that make it easy to pinpoint the location and its status of the locomotive.

Published in International Journal of Sensors and Sensor Networks (Volume 5, Issue 3)
DOI 10.11648/j.ijssn.20170503.11
Page(s) 34-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

DGPS, Di-filter, IMM Algorithm, Model Matching, Tracking Surveillance Model

References
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[5] T. G. Lee, “Centralized Kalman filter with adaptive measurement fusion: its application to a GPS/SDINS integration system with an additional sensor,” International Journal of Control, Automation, and Systems, vol. 1, no. 4, pp. 444-452, December 2003.
[6] I. Simeonova et al., “Specific features of IMM tracking filter design,” An International Journal of Information and Security, vol. 9, pp. 154-165, 2009.
[7] Z. F. Syed, et al. Civilian Vehicle Navigation: Required Alignment of the Inertial Sensors for Acceptable Navigation Accuracies. IEEE Trans. Weh. Tech Nol., 57 (6): 30402 – 30412, 2008.
[8] J. H. Wang, et al. Land vehicle dynamics-aided inertial navigation. IEEE Trans. Aerospace. Electron. Syst, 46 (4): 1638-1653, 2010.
[9] X. Li et al. An adaptive fault tolerant multisensory navigation strategy for automated vehicles. IEEE Trans. Veh. Technol., 59 (6): 2815- 2829, 2010.
[10] H. Zhang et al. The performance comparison and analysis of first order EKF, Second Order EKF and smoother for GPS/DR navigation. Optik, 122: 777-781, 2011.
[11] R. R. Pinho, et al., “Comparison between Kalman and Unscented Kalman Filters in Tracking Applications of Computational Vision”, in Vip IMAGE 2009.
[12] S. J. Julier et al. “Reduced Sigma Point Filters for the Propagation of Means and Covariance’s Through nonlinear Transformations”, in Proc. American Control Conference Alaska, pp.887-892, USA, 2002.
[13] R. Merwe, et al. “The Unscented Kalman Filter Advances” in Neural Information Processing Systems 2010, Vancouver, Canada.
[14] S. Antonov, et al. “Unscented Kalman filter for vehicle state estimation,” Vehicle System Dynamics, vol. 49, no. 9, pp. 1497–1520, September 2011.
[15] A. Budiyono, et al. “Principles of optimal control with applications.” Lecture Notes on Optimal Control Engineering, Department of Aeronautics & Astronautics, Bandung Institute of Technology, 2004.
[16] Duncan, et al. “Approaches to multi sensor data fusion in target tracking: a survey. IEEE Trans. Knowl. Data Eng.” 2006, 18, 1696–1710.
[17] Yong et al. “An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment.” Vol.14, no, 8, 1523-1603, Elsevier, Network and computer application, 2015.
[18] Watson, et al. “IMM Algorithm for Tracking Tar¬gets That Maneuver Through Coordinated Turns,” SPIE—Signal and Data Processing of Small Targets 1698, 236–247 (2012).
Cite This Article
  • APA Style

    Tanuja Parameshwar Patgar, Shankaraiah. (2017). Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm. International Journal of Sensors and Sensor Networks, 5(3), 34-42. https://doi.org/10.11648/j.ijssn.20170503.11

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    ACS Style

    Tanuja Parameshwar Patgar; Shankaraiah. Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm. Int. J. Sens. Sens. Netw. 2017, 5(3), 34-42. doi: 10.11648/j.ijssn.20170503.11

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    AMA Style

    Tanuja Parameshwar Patgar, Shankaraiah. Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm. Int J Sens Sens Netw. 2017;5(3):34-42. doi: 10.11648/j.ijssn.20170503.11

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  • @article{10.11648/j.ijssn.20170503.11,
      author = {Tanuja Parameshwar Patgar and Shankaraiah},
      title = {Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm},
      journal = {International Journal of Sensors and Sensor Networks},
      volume = {5},
      number = {3},
      pages = {34-42},
      doi = {10.11648/j.ijssn.20170503.11},
      url = {https://doi.org/10.11648/j.ijssn.20170503.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20170503.11},
      abstract = {Locomotive surveillance is the most active research topic and still faces big technical challenges in railway safety control system. An end-to-end locomotive tracking and continuous monitoring system is necessary for safety measures in satellite visible and low satellite visible environment. These smart systems aim to updates the information on location, exact detection, speed limitation and also rail track information. This paper contributes to develop an intelligent tracking and monitoring system based on Internet of Things (IoT) platform using Differential Global Positioning System (DGPS) for improved tracking accuracy of locomotive in both environments. Interacting Multiple Model (IMM) tracking algorithm based on Di-filter model is proposed for analysis that make it easy to pinpoint the location and its status of the locomotive.},
     year = {2017}
    }
    

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    T1  - Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm
    AU  - Tanuja Parameshwar Patgar
    AU  - Shankaraiah
    Y1  - 2017/08/10
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    T2  - International Journal of Sensors and Sensor Networks
    JF  - International Journal of Sensors and Sensor Networks
    JO  - International Journal of Sensors and Sensor Networks
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    AB  - Locomotive surveillance is the most active research topic and still faces big technical challenges in railway safety control system. An end-to-end locomotive tracking and continuous monitoring system is necessary for safety measures in satellite visible and low satellite visible environment. These smart systems aim to updates the information on location, exact detection, speed limitation and also rail track information. This paper contributes to develop an intelligent tracking and monitoring system based on Internet of Things (IoT) platform using Differential Global Positioning System (DGPS) for improved tracking accuracy of locomotive in both environments. Interacting Multiple Model (IMM) tracking algorithm based on Di-filter model is proposed for analysis that make it easy to pinpoint the location and its status of the locomotive.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • SJCE Research Center, Mysore, India

  • Department of ECE, SJCE, Mysore, India

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