Alper Mustafa SelverMustafa SeçmenEmine Yeşim ZoralSelver, M. AlperZoral, E. YesimSecmen, Mustafa2025-10-06201797888907018702164-334210.23919/EuCAP.2017.79288312-s2.0-85020225404https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020225404&doi=10.23919%2FEuCAP.2017.7928831&partnerID=40&md5=cace8b2294bd1cf8aacbedf01b0effbahttps://gcris.yasar.edu.tr/handle/123456789/9685https://doi.org/10.23919/EuCAP.2017.7928831Target identification from scattered signals using time domain techniques depend significantly on the waveform. Recently a novel feature set is proposed which encounter structural properties of the waveform and collects local extrema points to model the scattered signal via triangularization. Then using this piecewise model it extracts several morphological features and employs them for target identification through classification. This study expands that approach by modeling the scattered signal with other geometric shapes and accordingly by enriching the feature set. Such an approach requires careful representation of the waveform model since more than one morphology is considered to represent sub-waves of the waveform. The effects of the proposed approach are observed by applications on spherical targets having different size and material type. © 2017 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessFeature Extraction, Geometric-modeling, Resonance Region, Scattered Signals, Target Identification, Feature Extraction, Geometry, Waveform Analysis, Geometric Modeling, Morphological Features, Piece-wise Models, Resonance Region, Scattered Signals, Target Identification, Time-domain Technique, Waveform Modeling, Time Domain AnalysisFeature extraction, Geometry, Waveform analysis, Geometric modeling, Morphological features, Piece-wise models, Resonance region, Scattered signals, Target identification, Time-domain technique, Waveform modeling, Time domain analysisScattered SignalsResonance RegionFeature ExtractionGeometric-modelingTarget IdentificationConference Object