Share Email Print
cover

Proceedings Paper

SVM based target classification using RCS feature vectors
Author(s): Travis D. Bufler; Ram M. Narayanan; Traian Dogaru
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 21 May 2015
PDF: 10 pages
Proc. SPIE 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI, 94610I (21 May 2015); doi: 10.1117/12.2176759
Show Author Affiliations
Travis D. Bufler, The Pennsylvania State Univ. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)
Traian Dogaru, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 9461:
Radar Sensor Technology XIX; and Active and Passive Signatures VI
G. Charmaine Gilbreath; Chadwick Todd Hawley; Kenneth I. Ranney; Armin Doerry, Editor(s)

© SPIE. Terms of Use
Back to Top