Support Vector Machine based micro-Doppler signature classification of ground targets

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...

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Bibliographic Details
Main Authors: Javed, Aamir, Liaqat, Sidrah, Bin Ihsan, Mojeeb
Format: Conference Proceeding
Language:English
Subjects:
Automatic target classification
Doppler radar
Feature extraction
Ground surveillance radar
Kernel
micro-Doppler
Principal component analysis
Pulsedoppler radar
Radar target recognition
Support Vector Machine
Support vector machine classification
Vehicles
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Summary: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%.
DOI:10.23919/EuMC.2013.6687035