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LAI Lei, QU Shi-ru. Moving vehicle location method based on traffic wireless sensor network[J]. Journal of Traffic and Transportation Engineering, 2013, 13(1): 114-120. doi: 10.19818/j.cnki.1671-1637.2013.01.017
Citation: LAI Lei, QU Shi-ru. Moving vehicle location method based on traffic wireless sensor network[J]. Journal of Traffic and Transportation Engineering, 2013, 13(1): 114-120. doi: 10.19818/j.cnki.1671-1637.2013.01.017
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Moving vehicle location method based on traffic wireless sensor network

doi: 10.19818/j.cnki.1671-1637.2013.01.017

  • School of Automation,Northwestern Polytechnical University,Xi'an 710072,Shaanxi,China

More Information
  • Author Bio:

    LAI Lei(1983-), male, doctoral student, +86-29-86914294, lailei@mail.nwpu.edu.cn

    QU Shi-ru(1963-), female, professor, PhD, +86-29-88431387, qushiru@nwpu.edu.cn

  • Received Date: 2012-10-23
  • Publish Date: 2013-02-25
    Abstract
  • To improve the location reliability and accuracy, moving vehicle location system based on traffic wireless sensor network was studied. Based on the law that vehicle location changed along with its speed, a variable interval quantum particle swarm optimization algorithm was proposed, by which the measured vehicle location parameters could be used for the rough estimation of vehicle coordinates. For the noise interferences and signal delay, the rough estimated values of vehicle coordinates were always prone to error. The current statistical model was introduced into the algorithm under the motion constraints of vehicle, and the extended Kalman filter was used to eliminate the location errors. The proposed method was tested by the evaluation indexes of speed and accuracy. Tested result indicates that the location reliability is improved for that the enormous sum nodes of wireless sensor network can be disposed. The variable interval introduced into the quantum particle swarm optimization increases the convergence speed by 39.13%. The Kalman filter corrects the errors, and improves location precision by 56.48%. The proposed algorithm demonstrates the superiority in terms of location reliability and accuracy.

     

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