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Proceedings Paper

Multiclass multiple kernel learning for HRRP-based radar target recognition
Author(s): Yu Guo; Huaitie Xiao; Hongqi Fan; Yongfeng Zhu
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Paper Abstract

A novel machine learning method named multiclass multiple kernel learning based on support vector data description with negative (MMKL-NSVDD) is developed to classify the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The proposed method not only inherits the close nonlinear boundary advantage of SVDD-neg model, which is applied with no assumptions regarding to the distribution of data and prior information, but also incorporates multiple kernel into the mode, avoiding fussy choice of kernel parameters and fusing multiple kernel information. Hence, it leads to a remarkable improvement of recognition rate, demonstrated by experimental results based on HRRPs of four aircrafts. The MMKL-NSVDD is ideal for HRRPBased radar target recognition.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044306 (19 June 2017);
Show Author Affiliations
Yu Guo, National Univ. of Defense Technology (China)
Huaitie Xiao, National Univ. of Defense Technology (China)
Hongqi Fan, National Univ. of Defense Technology (China)
Yongfeng Zhu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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