Naval target classification is one of the prominent area of research in defence to safeguard ships and to provide guidelines for shipping channels. This work mainly explains the machine learning approach for naval target classification by examining the radar kinematics. The Artificial Neural Network (ANN) model is developed to classify various ship models. The Radar Cross-Section (RCS) data has been used for identification and classification of the naval target. The RCS database for ships are generated by simulating the open domain CATIA models.
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Machine-Learning for Classification of Naval Targets
Semantic Scholar · Engineering · 2019
Abstract
Naval target classification is one of the prominent area of research in defence to safeguard ships and to provide guidelines for shipping channels. This work mainly explains the machine learning approach for naval target classification by examining the radar kinematics. The Artificial Neural Network (ANN) model is developed to classify various ship models. The Radar Cross-Section (RCS) data has been used for identification and classification of the naval target. The RCS database for ships are generated by simulating the open domain CATIA models.