Particle Filter Parallel of Improved Algorithm Based on OpenMp

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 279)

Abstract

Particle filter was not limited by the system model and noise distribution, it was more consistent with the requirements of the actual filtering task, therefore, it received extensive attention in dynamic system problems of non-linear and non-Gaussian filtering. However, pointed at the complexity of the filter tracking and the increasing requirements for accuracy, the traditional non-linear filtering method was difficult to meet the needs of practical application. To solve this problem, this paper proposed a particle filter parallel of improved algorithm based on OpenMp, by parallelization improvement of traditional particle filter algorithm, mapping on each processors to run simultaneously of each stages in the model in the form of threads in parallelization, thus, made the video frame to be pipelined. The simulation showed that the proposed algorithm could effectively improve the advantages of performance in programs, made full use of computing resources, improved the filtering accuracy, and made the particle filter to be more widely used.

Keywords

OpenMp particle filter parallel technology target tracking 

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References

  1. 1.
    Wu, X.-T., Deng, J.-X., Ren, Y.-L., Yang, Y.: Multiple description of compressed sensing parallel processing algorithm based on OpenMP. Application Research of Computers 30(4), 1278–1280 (2013)Google Scholar
  2. 2.
    Zhu, X.-M., Pan, J.-S., Sun, Z.-Q., Gu, W.-D.: Parallel Implementation and Optimization of Two Basic Geo-SpatiaI-Analysis Algorithms Based on OpenMP. Computer Science 40(2), 8–11 (2013)Google Scholar
  3. 3.
    Fu, H.-Y., Ding, Y., Song, W., Yang, X.-J.: Fault Tolerance Scheme Using Parallel Recomputing for OpenMP Programs. Journal of Software 23(2), 411–427 (2012)CrossRefGoogle Scholar
  4. 4.
    Pan, X.-M., Pi, W.-C., Sheng, X.-Q.: Efficient Parallelization of Multilevel Fast Multipole Algorithm Based on OpenMP. Transactions of Beijing Institute of Technology 32(2), 164–169 (2012)Google Scholar
  5. 5.
    Ren, X.-X., Tang, L., Li, R.-F., Ling, C.-Q.: Study and Implementation of OpenMP Multi-Thread Load Balance Scheduling Scheme. Computer Science 32(2), 164–169 (2012)Google Scholar
  6. 6.
    Zhang, J., Deng, J.: Target Tracking Algorithm based on Gray System Theory and Particle Filter. Computer Application and Software 30(4), 131–134 (2013)CrossRefGoogle Scholar
  7. 7.
    Yi, L., Ya, E.: Study of Color Image Contour Extraction Algorithm based on Particle Filter. Computer Simulation 30(3), 384–388 (2013)Google Scholar
  8. 8.
    Chan, T.-Q., Li, Y., Liu, Z.-R., Dong, T.-Z.: An Improved Resampling Particle Filtering Algorithm. Application Research of Computers 30(3), 748–750 (2013)Google Scholar
  9. 9.
    Wan, Y., Wang, S.-Y.: Particle Filter m ethod based on Recursive Bayes Model. Journal of Signal Processing (2), 152–158 (2013)Google Scholar
  10. 10.
    Zhang, D.-W., Sun, L., Liu, D.: Track-before-Detect Algorithm Based on Particle Filter. Computer Simulation 29(11), 264–267 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Software CollegeJilin UniversityDaqingChina

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