Abstract Radar target recognition technology is a hot topic in modern radar research field. It can provide target information for the instructor or the operator to make the right decision. Due to its structural feature information, high-resolution range profile (HRRP) is widely used in the field of radar automatic target recognition (RATR). In this paper, we introduced a classification method based on kernel principal component analysis and collaborative representation (KPCA_CRC). First, KPCA is used to extract the nonlinear structure of target data and to reduce the data dimensions of the sample. Then, collaborative representation of samples is carried out to further improve the accuracy of target recognition. Experiments have been done on airplane data of a domestic institution. Compared with Fisher discriminant dictionary learning (FDDL) and the algorithm of CRC_RLS, the experimental results of the method of KPCA_CRC show better performance.


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    Title :

    Radar Target Recognition Method Based on Kernel Principal Component Analysis and Collaborative Representation


    Contributors:
    Guo, Zhiqiang (author) / Wu, Keming (author) / Liu, Lan (author) / Huang, Jing (author)


    Edition :

    1st ed. 2016


    Publication date :

    2016-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English