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Authors: Guy Ardon 1 ; 2 ; Or Simko 1 ; 2 and Akiva Novoselsky 2

Affiliations: 1 Department of Electrical & Computer Engineering, Ben-Gurion University of the Negev, Beersheba, Israel ; 2 ELTA Systems Ltd. Group & Subsidiary of Israel Aerospace Industries Ltd., Ashdod, 771020, Israel

Keyword(s): Aerial Radar Target Classification, Radar Cross Section (RCS), Time-Series Classification, Fully-Connected Neural Networks, Empirical Mode Decomposition (EMD).

Abstract: In this paper, we propose a new algorithm for classification of aerial radar targets by using Radar Cross Section (RCS) time-series corresponding to target detections of a given track. RCS values are obtained directly from SNR values, according to the radar equation. The classification is based on analysing the behaviour of the RCS time-series, which is the unique “fingerprint” of an aerial radar target. The classification process proposed in this paper is based on training a fully-connected neural network on features extracted from the RCS time-series and its corresponding Intrinsic Mode Functions (IMFs). The training is based on a database containing RCS signatures of various aerial targets. The algorithm has been tested on a large and diverse set of simulative flight trajectories, and its performance has been compared with that of several different methods. We have found that the proposed neural network-based classifier performed better on our database.

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Paper citation in several formats:
Ardon, G.; Simko, O. and Novoselsky, A. (2020). Aerial Radar Target Classification using Artificial Neural Networks. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-397-1; ISSN 2184-4313, SciTePress, pages 136-141. DOI: 10.5220/0008911701360141

@conference{icpram20,
author={Guy Ardon. and Or Simko. and Akiva Novoselsky.},
title={Aerial Radar Target Classification using Artificial Neural Networks},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2020},
pages={136-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008911701360141},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Aerial Radar Target Classification using Artificial Neural Networks
SN - 978-989-758-397-1
IS - 2184-4313
AU - Ardon, G.
AU - Simko, O.
AU - Novoselsky, A.
PY - 2020
SP - 136
EP - 141
DO - 10.5220/0008911701360141
PB - SciTePress

- Science and Technology Publications, Lda.
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