Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (11): 2433-.doi: 10.3969/j.issn.1001-506X.2018.11.07

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Radar HRRP target recognition based on convolutional sparse coding and multi-classifier fusion

WANG Caiyun1, HU Yunkan1, LI Xiaofei2, WEI Wenyi1, ZHAO Huanyue1   

  1. 1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 2. Beijing Institute of Electronic System Engineering, Beijing 100854, China
  • Online:2018-10-25 Published:2018-11-14

Abstract:

A radar high resolution range profile (HRRP) target recognition algorithm based on convolutional sparse coding and classifier fusion method, named convolutional sparse coding and multi-classifier fusion (CSCMF) is proposed. Firstly, it extracts the features from the HRRPs using the convolutional sparse coding (CSC) method, and realizes the compression of the data set. Secondly, three different classifiers (random forest classifier, naive Bayesian classifier, and minimum classifier) that fuse the sparse coding characteristics were used to obtain three predictive labels. Finally, we adopt classifier fusion by the majority of the voting methods to get the final recognition decision. We researched some classifiers algorithms in our experiments, and the simulation results based on the radar high resolution range profile database demonstrate the presented method can achieve remarkable classification performance and more robust to noise.

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