Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 1293
• Main Authors: Araujo, Gustavo F., Machado, Renato, Pettersson, Mats I.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2022; MDPI
• Subjects: Algorithms; Analysis; Artificial satellites in remote sensing; Automatic target recognition; Classification; Classification (of information); Classification algorithm; Data mining; Datasets; Discriminant analysis; Hypotheses; Morphology; Non-cooperative; Non-cooperative target; Pattern Recognition, Automated; Radar; Radar data; Radar target recognition; Recognition, Psychology; Scattering center; Scattering center models; Scattering centers; Statistical tests; Synthetic aperture radar; Synthetic aperture radar automatic target recognition; Synthetic aperture radar images; Synthetic data; Target recognition; Target recognition algorithms; scattering center; synthetic aperture radar; automatic target recognition; classification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22031293

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.