Classification of Ground Moving Radar Targets with RBF Neural Networks

Eran Notkin, Tomer Cohen, Akiva Novoselsky

2019

Abstract

This paper presents a novel method for classification of targets detected by Ground Moving Target Indication (GMTI) radar systems. GMTI radar systems provide no direct information regarding the type or size of the detected targets. The suggested method allow classification of ground moving targets into few groups of size, by analysis of Signal to Noise Ratio (SNR) values of GMTI radar measurements. The classification method is based on Radial Basis Function (RBF) neural networks. The data used as features for classification composed of Radar Cross Section (RCS) values of the target (obtained from the SNR values) in varying aspect angles. The proposed classifier was tested on diverse simulative cases and yielded very good results in classification of targets for three groups of size.

Paper Citation

in Harvard Style

Notkin E., Cohen T. and Novoselsky A. (2019). Classification of Ground Moving Radar Targets with RBF Neural Networks.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 328-333. DOI: 10.5220/0007254203280333

in Bibtex Style

@conference{icpram19,
author={Eran Notkin and Tomer Cohen and Akiva Novoselsky},
title={Classification of Ground Moving Radar Targets with RBF Neural Networks},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={328-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007254203280333},
isbn={978-989-758-351-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Classification of Ground Moving Radar Targets with RBF Neural Networks
SN - 978-989-758-351-3
AU - Notkin E.
AU - Cohen T.
AU - Novoselsky A.
PY - 2019
SP - 328
EP - 333
DO - 10.5220/0007254203280333