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BP Neural Network Algorithm for Multi-sensor Trajectory Separation Based on Maximum Membership Degree

  • Chao Sun
  • Qinquan Gao
  • Xinhui Li
  • Zhifeng Luo
  • Xiaolin Liu
Part of the Communications in Computer and Information Science book series (CCIS, volume 225)

Abstract

Aiming at the problem of trajectory separation which belongs to the data fusion technology, the multi-sensor trajectory separation algorithm of BP neural network based on the maximum membership degree is presented in this paper. The trajectory points can be predicted by establishing the trajectory prediction model which is based on the BP neural network, and the new radar data can be judged whether they belong to the prescriptive trajectory; Based on the prediction of the BP neural network, the multi-trajectory separation algorithm of fuzzy clustering of maximum membership degree is added to improve the effectiveness and accuracy of the trajectory separation. The experimental tests show that the algorithm which is presented in this paper effectively improves the efficiency and accuracy of the trajectory separation, and has a better application value.

Keywords: BP neural network, maximum membership degree, fuzzy clustering, mean square error.

Keywords

Membership Function Fuzzy Cluster Membership Degree Fusion Center Wavelet Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chao Sun
    • 1
  • Qinquan Gao
    • 1
  • Xinhui Li
    • 1
  • Zhifeng Luo
    • 1
  • Xiaolin Liu
    • 1
  1. 1.Department of AutomationXiamen UniversityXiamenChina

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