Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm
International Journal of Sensors and Sensor Networks
Volume 5, Issue 3, June 2017, Pages: 34-42
Received: Jun. 4, 2017;
Accepted: Jun. 26, 2017;
Published: Aug. 10, 2017
Views 1819 Downloads 90
Tanuja Parameshwar Patgar, SJCE Research Center, Mysore, India
Shankaraiah, Department of ECE, SJCE, Mysore, India
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.
Tanuja Parameshwar Patgar,
Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm, International Journal of Sensors and Sensor Networks.
Vol. 5, No. 3,
2017, pp. 34-42.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
U. S. Coast Guard Navigation Centre, NAVSTAR GPS user equipment introduction (Aug 1, 2011).
AGUADO, L, et al.: “A low Cost Low Power GPS Positioning system for monitoring Landslide” NAVI Tech 2007.
Will HEDGCOCK et al. “High accuracy difference tracking of low cost GPS receive”, Elsevier 2011.
M. A. HANNAN et al. “Intelligent bus monitoring and management system”. IEEE vehicular communication journal, 2012.
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.
I. Simeonova et al., “Specific features of IMM tracking filter design,” An International Journal of Information and Security, vol. 9, pp. 154-165, 2009.
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.
J. H. Wang, et al. Land vehicle dynamics-aided inertial navigation. IEEE Trans. Aerospace. Electron. Syst, 46 (4): 1638-1653, 2010.
X. Li et al. An adaptive fault tolerant multisensory navigation strategy for automated vehicles. IEEE Trans. Veh. Technol., 59 (6): 2815- 2829, 2010.
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.
R. R. Pinho, et al., “Comparison between Kalman and Unscented Kalman Filters in Tracking Applications of Computational Vision”, in Vip IMAGE 2009.
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.
R. Merwe, et al. “The Unscented Kalman Filter Advances” in Neural Information Processing Systems 2010, Vancouver, Canada.
S. Antonov, et al. “Unscented Kalman filter for vehicle state estimation,” Vehicle System Dynamics, vol. 49, no. 9, pp. 1497–1520, September 2011.
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.
Duncan, et al. “Approaches to multi sensor data fusion in target tracking: a survey. IEEE Trans. Knowl. Data Eng.” 2006, 18, 1696–1710.
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.
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).