UWB target classification using SVM

MAGDALENA BOUZA; ANDRÉS ALTIERI; CECILIA G. GALARZA

San Miguel de Tucuman

Congreso; 2018 IEEE Biennial Congress of Argentina (ARGENCON); 2018

Ultra-Wideband(UWB)radarsignalsarecharacterized for having both high frequency carrier andhigh bandwidth. This makes the scattered field from thetargets when irradiated with UWB pulses highly dependentof the composition and shape of the target. Our goal is toclassify objects by their composition from their scatteredresponses. In this paper, we propose to use a Support Vector Machine (SVM) to solve the problem for distinct dielectricmaterials and sphere elements. For a problem consideringMdifferent materials andRradii, we compare performance of three different SVM configurations. The first one considersthe general problem where each class corresponds to adifferent material. In this approach, each class is trained withdata corresponding to all Rradii. On a second approach,we classify by both radii and material. This gives a largerproblem to solve, where the number of classes of the SVM is M×R+ 1. Finally, a third approach considers a cascadeof SVMs where the first layer consists of a SVM for R+ 1classes, each class associated with one radius, while the secondlayer is composed ofRdifferent SVMs, each correspondingto a different radius, that classify between theMmaterials. Monte Carlo experiments are run to compare performanceamong the different proposed schemes. We analyze the resultsconsidering both classification and algorithmic complexity