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Techniques for target recognition in ballistic midcourse based on particle swarm-based intuitionistic fuzzy kernel c-means

YU Xiao-dong1, LEI Ying-jie1, MENG Fei-xiang1, LEI Yang2   

  1. 1. Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China; 
    2. Electronics Department, Engineering University of Armed Police Force, Xi’an 710086, China
  • Online:2015-01-13 Published:2010-01-03

Abstract:

The intuitionistic fuzzy clustering algorithms are sensitive to the initial value, easy to fall into local optimum and have slow convergence speed. To overcome these shortages, the particle swarm optimization(PSO)algorithm with powerful ability of global search and quick convergence rate is applied to intuitionistic fuzzy clustering. Firstly, PSO is used to optimize the initial clustering centers.Then,the approach of intuitionistic fuzzy kernel c-means(IFKCM)based on PSO, namely PS-IFKCM, is proposed. Then, experiments based on four measured datasets are carried out to illustrate the performance of the proposed method. Subsequently, the tactical ballistic missile (TBM) target recognition experiment is carried out based on radar cross section (RCS), which is usually applied in target recognition in the middle ballistic trajectory. Compared with results from fuzzy c-means and IFKCM, PS-IFKCM is of great efficiency when it comes to target recognition in the middle ballistic trajectory.

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