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Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models.
Araujo, Gustavo F; Machado, Renato; Pettersson, Mats I.
Afiliação
  • Araujo GF; Aeronautics Institute of Technology, São José dos Campos 12228-900, Brazil.
  • Machado R; Aeronautics Institute of Technology, São José dos Campos 12228-900, Brazil.
  • Pettersson MI; Blekinge Institute of Technology, 371 79 Karlskrona, Sweden.
Sensors (Basel) ; 22(3)2022 Feb 08.
Article em En | MEDLINE | ID: mdl-35162039
This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than -5 dB.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radar / Reconhecimento Automatizado de Padrão Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Radar / Reconhecimento Automatizado de Padrão Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça