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

Affiliation of Author(s):航天学院

Journal:Xi Tong Cheng Yu Dian Zi Ji Shu/Syst Eng Electron

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. © 2018, Editorial Office of Systems Engineering and Electronics. All right reserved.

ISSN No.:1001-506X

Translation or Not:no

Date of Publication:2018-11-01

Co-author:Hu, Yunkan,Li, Xiaofei,Wei, Wenyi,Zhao, Huanyue

Correspondence Author:王彩云,Hu, Yunkan,wcy

Date of Publication:2018-11-01