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Proceedings Paper

Feature analysis for indoor radar target classification
Author(s): Travis D. Bufler; Ram M. Narayanan
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Paper Abstract

This paper analyzes the spectral features from human beings and indoor clutter for building and tuning Support Vector Machines (SVMs) classifiers for the purpose of classifying stationary human targets. The spectral characteristics were obtained through simulations using Finite Difference Time Domain (FDTD) techniques where the radar cross section (RCS) of humans and indoor clutter objects were captured over a wide range of frequencies, polarizations, aspect angles, and materials. Additionally, experimental data was obtained using a vector network analyzer. Two different feature sets for class discrimination are used from the acquired target and clutter RCS spectral data sets. The first feature vectors consist of the raw spectral characteristics, while the second set of feature vectors are statistical features extracted over a set frequency interval. Utilizing variables of frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Classification accuracy over 80% can be effectively achieved given appropriate features.

Paper Details

Date Published: 12 May 2016
PDF: 9 pages
Proc. SPIE 9829, Radar Sensor Technology XX, 98290Y (12 May 2016); doi: 10.1117/12.2224041
Show Author Affiliations
Travis D. Bufler, The Pennsylvania State Univ. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 9829:
Radar Sensor Technology XX
Kenneth I. Ranney; Armin Doerry, Editor(s)

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