Machine-Learning for Classification of Naval Targets

被引：1

作者：

Sajjan, Sangeetha S. ^{[1
]}

Bhumika, C. S. ^{[1
]}

Choudhury, Balamati ^{[1
]}

Nair, Raveendranath U. ^{[1
]}

机构：

[1] CSIR Natl Aerosp Labs, Ctr Electromagnet, Bengaluru, India

来源：

2019 IEEE MTT-S INTERNATIONAL MICROWAVE AND RF CONFERENCE (IMARC) | 2019年

关键词：

ANN; CATIA models; Classification; Naval targets; RCS; SHIP CLASSIFICATION;

D O I：

10.1109/imarc45935.2019.9118614

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：4

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