Probabilistic SVM for open set automatic target recognition on high range resolution radar data | Zendy

Jason D. Roos | Zendy; A.K. Shaw | Zendy

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Probabilistic SVM for open set automatic target recognition on high range resolution radar data

Author(s) -

Jason D. Roos,

A.K. Shaw

Publication year - 2017

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.2262840

Subject(s) - computer science , probabilistic logic , artificial intelligence , support vector machine , data set , range (aeronautics) , radar , pattern recognition (psychology) , automatic target recognition , set (abstract data type) , synthetic aperture radar , remote sensing , data mining , computer vision , telecommunications , programming language , materials science , composite material , geology

The Eigen-Template (ET) based closed-set feature extraction approach is extended to an open-set HRR-ATR framework to develop an Open Set Probabilistic Support Vector Machine (OSP-SVM) classifier. The proposed ET-OSP-SVM is shown to perform open set ATR on HRR data with 80% PCC for a 4-class MSTAR dataset.

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