SVM based target classification using RCS feature vectors

Travis Dale Bufler, Ram Mohan Narayanan, Traian Dogaru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XIX; and Active and Passive Signatures VI
EditorsArmin Doerry, Chadwick Todd Hawley, G. Charmaine Gilbreath, Kenneth I. Ranney
ISBN (Electronic)9781628415773
StatePublished - Jan 1 2015
EventRadar Sensor Technology XIX; and Active and Passive Signatures VI - Baltimore, United States
Duration: Apr 20 2015Apr 23 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherRadar Sensor Technology XIX; and Active and Passive Signatures VI
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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