RADAR AUTOMATIC TARGET RECOGNITION BASED ON SEQUENTIAL VANISHING COMPONENT ANALYSIS | Zendy

Shengqi Liu | Zendy; Ronghui Zhan | Zendy; Jun Zhang | Zendy; Zhuang Zhao-wen | Zendy

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RADAR AUTOMATIC TARGET RECOGNITION BASED ON SEQUENTIAL VANISHING COMPONENT ANALYSIS

Author(s) -

Shengqi Liu,

Ronghui Zhan,

Jun Zhang,

Zhuang Zhao-wen

Publication year - 2014

Publication title -

electromagnetic waves

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.437

H-Index - 89

eISSN - 1559-8985

pISSN - 1070-4698

DOI - 10.2528/pier14011608

Subject(s) - component (thermodynamics) , computer science , artificial intelligence , radar , pattern recognition (psychology) , automatic target recognition , speech recognition , synthetic aperture radar , telecommunications , physics , thermodynamics

To reduce the complexity of classifler design in radar automatic target recognition (RATR), a novel RATR method for high range resolution proflle (HRRP) is proposed. Linearly separable features are extracted with sequential vanishing component analysis (SVCA) which is implemented by flnding the generators of each approximately vanishing polynomial set, and target classiflcation is implemented with linear classiflers. Experiments are carried out on simulated vehicle target data and MSTAR database, and the results demonstrate the e-ciency of the proposed method.

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