@inproceedings{f8ed4e0c0dac43fb803db0788cd0a983,
title = "SVM based target classification using RCS feature vectors",
abstract = "This paper investigates the application of SVM (Support Vector Machines) for the classification of stationary human targets and indoor clutter via spectral features. Applying Finite Difference Time Domain (FDTD) techniques allows us to examine the radar cross section (RCS) of humans and indoor clutter objects by utilizing different types of computer models. FDTD allows for the spectral characteristics to be acquired over a wide range of frequencies, polarizations, aspect angles, and materials. The acquired target and clutter RCS spectral characteristics are then investigated in terms of their potential for target classification using SVMs. Based upon variables such as frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Furthermore, the application of feature selection is applied to the spectral characteristics to determine the SVM classification accuracy of a reduced dataset. Classification accuracies of nearly 90% are achieved using radial and polynomial kernels.",
author = "Bufler, {Travis Dale} and Narayanan, {Ram Mohan} and Traian Dogaru",
year = "2015",
month = jan,
day = "1",
doi = "10.1117/12.2176759",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Armin Doerry and Hawley, {Chadwick Todd} and Gilbreath, {G. Charmaine} and Ranney, {Kenneth I.}",
booktitle = "Radar Sensor Technology XIX; and Active and Passive Signatures VI",
address = "United States",
note = "Radar Sensor Technology XIX; and Active and Passive Signatures VI ; Conference date: 20-04-2015 Through 23-04-2015",
}