Ceyhan TurkmenMustafa SecmenY Averyanova2025-10-062016978-1-5090-1050-9https://gcris.yasar.edu.tr/handle/123456789/6111This paper presents a radar target classification method using high-resolution Min-Norm (minimum norm) technique to discriminate targets in a selected target database. The method is convenient for the targets at the resonance scattering region where the length and other dimensions of the targets are on the order of the wavelength range of the incident fields. The method mainly needs certain number of reference scattered fields obtained from different cases (aspect angle/polarization) for each target. These gathered scattered fields are then processed with Min-norm method and averaged in order to construct one specific Min-norm vector for every targets. These vectors which contain valuable information about resonance frequencies (poles) of the targets are selected as the main features for targets and stored for the classification. In the classification phase a decision based on template matching approach is given according to similarity between test Min-norm vector and feature vectors. The proposed method is demonstrated for wire-based small-scale models of airplane targets and sufficiently high correct decision rates are achieved even for very noisy signals.Englishradar target classification, resonace scattering, Min-norm method, singularity expansion methodIDENTIFICATIONConference Object