吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 822-827.doi: 10.13229/j.cnki.jdxbgxb201403039

• Orignal Article • Previous Articles     Next Articles

Special object recognition based on sparse representation in multiclass fusion sample

ZHA Chang-jun1,2,WEI Sui1,YANG Hai-rong3,DING Da-wei1   

  1. 1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039,China;
    2.Key Laboratory of Machine Vision and Intelligence Control Technology, Hefei University, Hefei 230601,China;
    3.Department of Mathematics, Hefei Normal University,Hefei 230061,China
  • Received:2013-02-26 Online:2014-03-01 Published:2014-03-01

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Abstract: According to the characteristics of Wireless Sensor Network (WSN) and the profile detecting system, a data processing method based on WSN is proposed. In this method, first, the sample features are extracted by principal component analysis; then, the features of different samples were fused using accumulate mode. A mathematical model was given and on the basis of this model, a novel algorithm of special object recognition based on sparse representation in multiclass fusion sample was proposed. This algorithm recognizes the special target according to distribution of the main non-zero coefficients under an over-complete dictionary. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm, the comprehensive performance is better than the traditional methods.

Key words: information processing, wireless sensor network, sparse representation, profiling recognition, unattended ground sensor

CLC Number: 

  • TN911.74
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