Support Vector Machine Based Micro-Doppler Signature Classification of Ground Targets

被引：0

作者：

Javed, Aamir ^{[1
]}

Liaqat, Sidrah ^{[1
]}

Bin Ihsan, Mojeeb ^{[1
]}

机构：

[1] Natl Univ Sci & Technol, Coll Elect & Mech Engn, Islamabad, Pakistan

来源：

2013 10TH EUROPEAN RADAR CONFERENCE (EURAD) | 2013年

关键词：

Automatic target classification; micro-Doppler; Ground surveillance radar; Pulsedoppler radar; Radar target recognition; Principal component analysis; Support Vector Machine; RADAR;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, design of a micro-Doppler signature classifier for NR-V3 Ground Surveillance Radar* is discussed. The classifier distinguishes between pedestrians, vehicles and no target (noise) classes. Feature vector inputs for the classifier are extracted by preprocessing the FFT spectrum of radar backscattered signal. Support Vector Machine (SVM) with Radial Basis Function (RBF) and Polynomial kernels is used for classification of feature vectors. The classifiers are trained and tested using data collected with NR-V3 radar. This technique achieves a classification accuracy of over 94%.

引用

页码：515 / 518

页数：4

共 14 条

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